From 5743fa5918529cc5bb41d56084a15fc31fa3ba14 Mon Sep 17 00:00:00 2001 From: Josh Izaac Date: Mon, 14 Nov 2022 20:33:23 -0500 Subject: [PATCH 01/24] Development branch --- requirements.txt | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/requirements.txt b/requirements.txt index 31a775730e..d67ec324af 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,12 +17,12 @@ cirq-core==0.14.1 cirq-pasqal==0.14.1 openfermionpyscf==0.5 # required by 2 tutorials: tutorial_quantum_chemistry and tutorial_adaptive_circuits and tutorial_diffable_shadows pennylane-lightning -pennylane==0.27.0 -pennylane-sf==0.20.1 -pennylane-cirq==0.27.0 -pennylane-qiskit==0.27.0 +git+https://github.com/PennyLaneAI/pennylane.git +git+https://github.com/PennyLaneAI/pennylane-sf.git +git+https://github.com/PennyLaneAI/pennylane-cirq.git +git+https://github.com/PennyLaneAI/pennylane-qiskit.git qulacs==0.1.10.1 -pennylane-qulacs==0.24.0 +git+https://github.com/PennyLaneAI/pennylane-qulacs.git scikit-learn==0.23.2 docutils==0.16 markupsafe==2.0.1 From a8284e3b65c67b02774a8469ad495ec16e66cd98 Mon Sep 17 00:00:00 2001 From: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Date: Mon, 19 Dec 2022 15:47:32 -0500 Subject: [PATCH 02/24] Bumped requirement package versions (#667) * Bumped jax versions * Updated neural-tangents --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index ecdab7aae1..c0cf2b1ec2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -42,9 +42,9 @@ matplotlib==3.5.3 noisyopt==0.2.2 Jinja2==2.11.3 pyyaml==5.4.1 -jax==0.3.15 -jaxlib==0.3.15 -neural-tangents==0.6.0 +jax==0.4.1 +jaxlib==0.4.1 +neural-tangents==0.6.1 flamingpy>=0.10.1b1 plotly>=4.5.0 kahypar==1.1.7 From ccfcf459a29f6b38d520a8f8469aef4acccf8e3b Mon Sep 17 00:00:00 2001 From: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Date: Wed, 21 Dec 2022 11:54:52 -0500 Subject: [PATCH 03/24] Merge dev in master following v0.28.0 release of PennyLane (#671) (#672) * Development branch * Bumped requirement package versions (#667) * Bumped jax versions * Updated neural-tangents * Apply suggestions from code review Co-authored-by: Josh Izaac Co-authored-by: GitHub Nightly Merge Action Co-authored-by: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Co-authored-by: Jay Soni Co-authored-by: Josh Izaac Co-authored-by: GitHub Nightly Merge Action From 8356ccaea7c2485bc3cad5da0fc2e57c0ed32f42 Mon Sep 17 00:00:00 2001 From: soranjh <40344468+soranjh@users.noreply.github.com> Date: Tue, 21 Feb 2023 16:51:18 -0500 Subject: [PATCH 04/24] Update chemical reactions demo to use pyscf for openshell H3 (#704) * use pyscf for openshell H3 * change date and fix interface warning --- demonstrations/tutorial_chemical_reactions.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/demonstrations/tutorial_chemical_reactions.py b/demonstrations/tutorial_chemical_reactions.py index ce29d79acd..e6c048aa04 100644 --- a/demonstrations/tutorial_chemical_reactions.py +++ b/demonstrations/tutorial_chemical_reactions.py @@ -10,7 +10,7 @@ tutorial_quantum_chemistry Building molecular Hamiltonians tutorial_vqe A brief overview of VQE -*Authors: Varun Rishi and Juan Miguel Arrazola — Posted: 23 July 2021. Last updated: 23 July 2021.* +*Authors: Varun Rishi and Juan Miguel Arrazola — Posted: 23 July 2021. Last updated: 21 February 2023.* The term "chemical reaction" is another name for the transformation of molecules -- the breaking and forming of bonds. They are characterized by an energy barrier that determines @@ -129,13 +129,13 @@ coordinates = np.array([0.0, 0.0, 0.0, 0.0, 0.0, r]) # Obtain the qubit Hamiltonian - H, qubits = qchem.molecular_hamiltonian(symbols, coordinates) + H, qubits = qchem.molecular_hamiltonian(symbols, coordinates, method='pyscf') # define the device, optimizer and circuit dev = qml.device("default.qubit", wires=qubits) opt = qml.GradientDescentOptimizer(stepsize=0.4) - @qml.qnode(dev) + @qml.qnode(dev, interface='autograd') def circuit(parameters): # Prepare the HF state: |1100> qml.BasisState(hf, wires=range(qubits)) @@ -283,12 +283,12 @@ def circuit(parameters): coordinates = np.array([0.0, 0.0, 0.0, 0.0, 0.0, r, 0.0, 0.0, 4.0]) # We now specify the multiplicity - H, qubits = qchem.molecular_hamiltonian(symbols, coordinates, mult=multiplicity) + H, qubits = qchem.molecular_hamiltonian(symbols, coordinates, mult=multiplicity, method='pyscf') dev = qml.device("default.qubit", wires=qubits) opt = qml.GradientDescentOptimizer(stepsize=1.5) - @qml.qnode(dev) + @qml.qnode(dev, interface='autograd') def circuit(parameters): AllSinglesDoubles(parameters, range(qubits), hf, singles, doubles) return qml.expval(H) # we are interested in minimizing this expectation value From 10d6d86def924de3f453e3b2f4682d00d96e1f55 Mon Sep 17 00:00:00 2001 From: Romain Moyard Date: Wed, 22 Feb 2023 13:14:13 -0500 Subject: [PATCH 05/24] Change ibm device in Quantum volume (#703) * Ibm oslo * Update to new hardware * Change to Lima * Typo * Replace * One below --- demonstrations/quantum_volume.py | 148 +- .../quantum_volume/{ourense.svg => lima.svg} | 0 .../lima_heavy_output_distributions.svg | 2241 +++++++++++++++++ .../ourense_heavy_output_distributions.svg | 2127 ---------------- 4 files changed, 2314 insertions(+), 2202 deletions(-) rename demonstrations/quantum_volume/{ourense.svg => lima.svg} (100%) create mode 100644 demonstrations/quantum_volume/lima_heavy_output_distributions.svg delete mode 100644 demonstrations/quantum_volume/ourense_heavy_output_distributions.svg diff --git a/demonstrations/quantum_volume.py b/demonstrations/quantum_volume.py index f3c33be50d..87f7ec93b7 100644 --- a/demonstrations/quantum_volume.py +++ b/demonstrations/quantum_volume.py @@ -397,38 +397,38 @@ def qv_circuit_layer(num_qubits): # # .. code-block:: none # -# 0: ─╭SWAP─╭U(M0)─╭U(M1)─╭SWAP───────╭U(M2)─┤ -# 1: ─╰SWAP─╰U(M0)─╰U(M1)─│─────╭SWAP─╰U(M2)─┤ -# 2: ─────────────────────╰SWAP─╰SWAP────────┤ -# 3: ────────────────────────────────────────┤ -# 4: ────────────────────────────────────────┤ -# M0 = -# [[-0.17514647+0.00759447j 0.11975927+0.16007614j -0.41793925+0.49643728j -# 0.62304058-0.34640531j] -# [-0.73367896-0.58079555j -0.11348577+0.00751965j -0.02640159-0.15592112j -# -0.19507153-0.21998821j] -# [ 0.02988983+0.09364586j -0.74053162+0.55032455j 0.31350059-0.01305651j -# 0.16283233-0.11885036j] -# [-0.13103809-0.25850305j 0.18298996+0.2497364j 0.34879438+0.57771772j -# -0.02385446+0.60346274j]] -# M1 = -# [[ 0.14296171+0.28087257j -0.5985737 -0.27489922j -0.43838149+0.10344812j -# 0.04022491+0.51216658j] -# [-0.21538853+0.02728431j -0.24776721-0.57146257j 0.60975755+0.36241573j -# 0.21787038-0.11953391j] -# [-0.24405375+0.05780278j -0.11688629-0.17397518j -0.51628349-0.11381455j -# 0.44143429-0.64714776j] -# [-0.750841 -0.47630904j -0.28666068+0.22820556j -0.09459735+0.07429451j -# -0.17243398+0.17582253j]] -# M2 = -# [[-0.63733359+1.91519046e-01j -0.49615702+9.79920998e-05j -# 0.06949634+4.54968771e-01j 0.21112196-2.33571716e-01j] -# [ 0.4774216 +5.19692450e-02j -0.2741782 -3.71778068e-01j -# 0.09817361+6.01972062e-01j -0.39517581+1.66741872e-01j] -# [ 0.14401687-1.53582182e-01j 0.51636466-1.58216631e-01j -# 0.43804144+3.62586089e-01j 0.4473567 -3.74872915e-01j] -# [ 0.51670588+1.23210608e-01j -0.48982566-9.40288988e-02j -# -0.19210465-2.36457367e-01j 0.53202679-3.05278186e-01j]] +# 0: ─╭SWAP───────╭U(M0)─╭SWAP───────╭U(M1)─╭U(M2)─┤ +# 1: ─│─────╭SWAP─╰U(M0)─╰SWAP─╭SWAP─╰U(M1)─╰U(M2)─┤ +# 2: ─╰SWAP─╰SWAP──────────────╰SWAP───────────────┤ +# 3: ──────────────────────────────────────────────┤ +# 4: ──────────────────────────────────────────────┤ +# M0 = +# [[ 0.22234537+0.12795769j 0.24613682-0.34470179j 0.58179809-0.36478045j +# -0.16337007-0.50650086j] +# [-0.08840637-0.42456216j -0.01961572+0.35189839j 0.4214659 +0.31514336j +# -0.63733039+0.06764003j] +# [ 0.28919627-0.23577761j -0.11249786-0.67687982j 0.22914826+0.37205064j +# 0.12755164+0.42749545j] +# [ 0.59999195+0.49689511j -0.29294024+0.37382355j 0.23724315-0.06544043j +# -0.039832 +0.3246437j ]] +# M1 = +# [[ 0.11583153-0.3628563j 0.55797708+0.48315028j -0.22400838-0.264741j +# -0.34856401+0.26149824j] +# [-0.04549494-0.25884483j 0.00258749-0.20351027j -0.26326583-0.70408962j +# 0.33442905-0.46109931j] +# [-0.46824254-0.14274112j -0.00491681+0.61278881j -0.02506472+0.26582603j +# 0.54135395-0.14312156j] +# [ 0.73672445-0.05881259j 0.19534118+0.01057264j -0.29145879+0.398047j +# 0.33955583-0.23837031j]] +# M2 = +# [[-0.33352575+0.21982221j -0.29128941-0.51347253j 0.63543764-0.11913356j +# 0.27186717+0.00704727j] +# [-0.22330473+0.02289549j 0.1997405 -0.47316218j -0.23040621-0.14078015j +# -0.47922028-0.61909121j] +# [-0.00705247+0.82724695j 0.52220719+0.02527864j -0.05490671-0.04899343j +# 0.03167901+0.18935341j] +# [ 0.23396138-0.22566431j 0.32400589+0.09694607j 0.54666955-0.45261179j +# -0.48177768+0.2101061j ]] ############################################################################## @@ -510,19 +510,19 @@ def heavy_output_set(m, probs): # # .. code-block:: none # -# State Probability -# 000 0.0157 -# 001 0.0200 -# 010 0.0026 -# 011 0.2765 -# 100 0.0175 -# 101 0.4266 -# 110 0.0045 -# 111 0.2365 +# State Probability +# 000 0.0559 +# 001 0.3687 +# 010 0.0326 +# 011 0.0179 +# 100 0.0550 +# 101 0.3590 +# 110 0.1103 +# 111 0.0005 # -# Median is 0.0188 -# Probability of a heavy output is 0.9596 -# Heavy outputs are ['001', '111', '011', '101'] +# Median is 0.0554 +# Probability of a heavy output is 0.8939 +# Heavy outputs are ['000', '110', '101', '001'] # ############################################################################## @@ -531,7 +531,7 @@ def heavy_output_set(m, probs): # ~~~~~~~~~~~~~~~~~~~~~~~~ # # Now it's time to run the protocol. First, let's set up our hardware -# device. We'll use a simulated version of the 5-qubit IBM Ourense as an example +# device. We'll use a simulated version of the 5-qubit IBM Lima as an example # --- the reported quantum volume according to IBM is :math:`V_Q=8`, so we # endeavour to reproduce that here. This means that we should be able to run our # square circuits reliably on up to :math:`\log_2 V_Q =3` qubits. @@ -551,13 +551,10 @@ def heavy_output_set(m, probs): # # .. note:: # -# In the time since the original release of this demo, the Ourense device is -# no longer available from IBM Q. However, we leave the original results for -# expository purposes, and note that the methods are applicable in general. # Users can get a list of available IBM Q backends by importing IBM Q, # specifying their provider and then calling: ``provider.backends()`` # -dev_ourense = qml.device("qiskit.ibmq", wires=5, backend="ibmq_bogota") +dev_lima = qml.device("qiskit.ibmq", wires=5, backend="ibmq_lima") ############################################################################## # @@ -567,18 +564,18 @@ def heavy_output_set(m, probs): import matplotlib.pyplot as plt import networkx as nx -ourense_hardware_graph = nx.Graph(dev_ourense.backend.configuration().coupling_map) +lima_hardware_graph = nx.Graph(dev_lima.backend.configuration().coupling_map) nx.draw_networkx( - ourense_hardware_graph, + lima_hardware_graph, node_color="cyan", - labels={x: x for x in range(dev_ourense.num_wires)}, + labels={x: x for x in range(dev_lima.num_wires)}, ) ############################################################################## # -# .. figure:: ../demonstrations/quantum_volume/ourense.svg +# .. figure:: ../demonstrations/quantum_volume/lima.svg # :align: center # :width: 75% # @@ -589,16 +586,16 @@ def heavy_output_set(m, probs): # to make some adjustments when non-connected qubits need to interact. # # To actually perform the simulations, we'll need to access a copy of the -# Ourense noise model. Again, we won't be running on Ourense directly --- +# Lima noise model. Again, we won't be running on Lima directly --- # we'll set up a local device to simulate its behaviour. # from qiskit.providers.aer import noise -noise_model = noise.NoiseModel.from_backend(dev_ourense.backend) +noise_model = noise.NoiseModel.from_backend(dev_lima.backend) dev_noisy = qml.device( - "qiskit.aer", wires=dev_ourense.num_wires, shots=1000, noise_model=noise_model + "qiskit.aer", wires=dev_lima.num_wires, shots=1000, noise_model=noise_model ) ############################################################################## @@ -609,7 +606,7 @@ def heavy_output_set(m, probs): # qubit placement and routing techniques [#sabre]_ in order to fit the circuits # on the hardware graph in the best way possible. -coupling_map = dev_ourense.backend.configuration().to_dict()["coupling_map"] +coupling_map = dev_lima.backend.configuration().to_dict()["coupling_map"] dev_noisy.set_transpile_args( **{ @@ -697,24 +694,24 @@ def heavy_output_set(m, probs): # .. code-block:: none # # Ideal mean probabilities: -# m = 2: 0.797979 above threshold. -# m = 3: 0.844052 above threshold. -# m = 4: 0.841203 above threshold. -# m = 5: 0.856904 above threshold. +# m = 2: 0.801480 above threshold. +# m = 3: 0.853320 above threshold. +# m = 4: 0.832995 above threshold. +# m = 5: 0.858370 above threshold. # # Device mean probabilities: -# m = 2: 0.773760 above threshold. -# m = 3: 0.794875 above threshold. -# m = 4: 0.722860 above threshold. -# m = 5: 0.692935 above threshold. +# m = 2: 0.765920 above threshold. +# m = 3: 0.773985 above threshold. +# m = 4: 0.674380 above threshold. +# m = 5: 0.639500 below threshold. ############################################################################## # # We see that the ideal probabilities are well over 2/3. In fact, they're quite # close to the expected value of :math:`(1 + \ln 2)/2`, which we recall from # above is :math:`\approx 0.85`. For this experiment, we see that the device -# probabilities are also above the threshold. But it isn't enough that just the -# mean of the heavy output probabilities is greater than 2/3. Since we're +# probabilities are also above the threshold (except one). But it isn't enough +# that just the mean of the heavy output probabilities is greater than 2/3. Since we're # dealing with randomness, we also want to ensure these results were not just a # fluke! To be confident, we also want to be above 2/3 within 2 standard # deviations :math:`(\sigma)` of the mean. This is referred to as a 97.5% @@ -766,13 +763,14 @@ def heavy_output_set(m, probs): label="2σ", ) -fig.suptitle("Heavy output distributions for (simulated) Ourense QPU", fontsize=18) +fig.suptitle("Heavy output distributions for (simulated) Lima QPU", fontsize=18) plt.legend(fontsize=14) plt.tight_layout() + ############################################################################## # -# .. figure:: ../demonstrations/quantum_volume/ourense_heavy_output_distributions.svg +# .. figure:: ../demonstrations/quantum_volume/lima_heavy_output_distributions.svg # :align: center # :width: 90% # @@ -795,16 +793,16 @@ def heavy_output_set(m, probs): # # .. code-block:: none # -# m = 2: 0.714590 above threshold. -# m = 3: 0.737770 above threshold. -# m = 4: 0.659562 below threshold. -# m = 5: 0.627701 below threshold. +# m = 2: 0.706039 above threshold. +# m = 3: 0.714836 above threshold. +# m = 4: 0.608109 below threshold. +# m = 5: 0.571597 below threshold. # ############################################################################## # # We see that we are :math:`2\sigma` above the threshold only for :math:`m=2`, -# and :math:`m=3`. Thus, we find that the quantum volume of our simulated Ourense is +# and :math:`m=3`. Thus, we find that the quantum volume of our simulated Lima is # :math:`\log_2 V_Q = 3`, or :math:`V_Q = 8`, as expected. # # This framework and code will allow you to calculate the quantum volume of many @@ -831,7 +829,7 @@ def heavy_output_set(m, probs): # number of qubits continues to increase and error rates are getting lower, # both of which imply that our square circuits will be growing in both width and # depth as time goes on. Eventually they will reach a point where they are no -# longer classical simulable and we will have to design new benchmarks. +# longer classical simulable, and we will have to design new benchmarks. # # Another limitation is that the protocol only looks at one type of circuit, # i.e., square circuits. It might be the case that a processor has very few diff --git a/demonstrations/quantum_volume/ourense.svg b/demonstrations/quantum_volume/lima.svg similarity index 100% rename from demonstrations/quantum_volume/ourense.svg rename to demonstrations/quantum_volume/lima.svg diff --git a/demonstrations/quantum_volume/lima_heavy_output_distributions.svg b/demonstrations/quantum_volume/lima_heavy_output_distributions.svg new file mode 100644 index 0000000000..0a12856aef --- /dev/null +++ b/demonstrations/quantum_volume/lima_heavy_output_distributions.svg @@ -0,0 +1,2241 @@ + + + + + + + + 2023-02-21T16:23:37.357171 + image/svg+xml + + + Matplotlib v3.6.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/demonstrations/quantum_volume/ourense_heavy_output_distributions.svg b/demonstrations/quantum_volume/ourense_heavy_output_distributions.svg deleted file mode 100644 index 759fcfbdc2..0000000000 --- a/demonstrations/quantum_volume/ourense_heavy_output_distributions.svg +++ /dev/null @@ -1,2127 +0,0 @@ - - - - - - - - - 2021-01-24T11:24:04.152682 - image/svg+xml - - - Matplotlib v3.3.1, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - From ab34e2aa5ed66240b7fef111f3486a558e8d1871 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Wed, 22 Feb 2023 16:17:50 -0500 Subject: [PATCH 06/24] Update demos to remove warnings (#705) * Added interface, removed deprecated functionality * Addressing PR comments --------- Co-authored-by: Romain Moyard --- demonstrations/tutorial_adaptive_circuits.py | 10 +-- demonstrations/tutorial_backprop.py | 14 ++-- demonstrations/tutorial_barren_plateaus.py | 4 +- demonstrations/tutorial_classical_kernels.py | 2 +- .../tutorial_classically_boosted_vqe.py | 4 +- demonstrations/tutorial_coherent_vqls.py | 6 +- .../tutorial_data_reuploading_classifier.py | 2 +- demonstrations/tutorial_diffable_shadows.py | 22 +++--- demonstrations/tutorial_differentiable_HF.py | 2 +- demonstrations/tutorial_doubly_stochastic.py | 6 +- .../tutorial_expressivity_fourier_series.py | 8 +- demonstrations/tutorial_falqon.py | 8 +- demonstrations/tutorial_givens_rotations.py | 10 +-- .../tutorial_kernel_based_training.py | 2 +- demonstrations/tutorial_kernels_module.py | 2 +- .../tutorial_local_cost_functions.py | 16 ++-- demonstrations/tutorial_mbqc.py | 16 ++-- .../tutorial_measurement_optimize.py | 14 ++-- demonstrations/tutorial_mol_geo_opt.py | 2 +- .../tutorial_noisy_circuit_optimization.py | 77 ++++++++++--------- demonstrations/tutorial_plugins_hybrid.py | 2 +- demonstrations/tutorial_qgrnn.py | 2 +- .../tutorial_quantum_natural_gradient.py | 14 ++-- demonstrations/tutorial_quanvolution.py | 2 +- demonstrations/tutorial_qubit_rotation.py | 4 +- demonstrations/tutorial_qubit_tapering.py | 14 ++-- demonstrations/tutorial_rosalin.py | 10 +-- demonstrations/tutorial_sc_qubits.py | 16 ++-- demonstrations/tutorial_testing_symmetry.py | 2 +- demonstrations/tutorial_tn_circuits.py | 10 +-- demonstrations/tutorial_trapped_ions.py | 14 ++-- .../tutorial_variational_classifier.py | 6 +- demonstrations/tutorial_vqe.py | 2 +- demonstrations/tutorial_vqe_qng.py | 4 +- demonstrations/tutorial_vqe_spin_sectors.py | 8 +- demonstrations/tutorial_vqls.py | 4 +- demonstrations/tutorial_vqt.py | 2 +- 37 files changed, 174 insertions(+), 169 deletions(-) diff --git a/demonstrations/tutorial_adaptive_circuits.py b/demonstrations/tutorial_adaptive_circuits.py index b40804500d..13e4649ff3 100644 --- a/demonstrations/tutorial_adaptive_circuits.py +++ b/demonstrations/tutorial_adaptive_circuits.py @@ -123,7 +123,7 @@ def circuit_1(params, excitations): # with respect to the Hartree-Fock state. dev = qml.device("default.qubit", wires=qubits) -cost_fn = qml.QNode(circuit_1, dev) +cost_fn = qml.QNode(circuit_1, dev, interface="autograd") circuit_gradient = qml.grad(cost_fn, argnum=0) @@ -181,7 +181,7 @@ def circuit_2(params, excitations, gates_select, params_select): ############################################################################## # We now compute the gradients for the single excitation gates. -cost_fn = qml.QNode(circuit_2, dev) +cost_fn = qml.QNode(circuit_2, dev, interface="autograd") circuit_gradient = qml.grad(cost_fn, argnum=0) params = [0.0] * len(singles) @@ -213,7 +213,7 @@ def circuit_2(params, excitations, gates_select, params_select): # We perform one final step of optimization to get the ground-state energy. The resulting energy # should match the exact energy of the ground electronic state of LiH which is -7.8825378193 Ha. -cost_fn = qml.QNode(circuit_1, dev) +cost_fn = qml.QNode(circuit_1, dev, interface="autograd") params = np.zeros(len(doubles_select + singles_select), requires_grad=True) @@ -239,7 +239,7 @@ def circuit_2(params, excitations, gates_select, params_select): # PennyLane function :func:`~.pennylane.utils.sparse_hamiltonian`. This function # returns the matrix in the SciPy `sparse coordinate `_ format. -H_sparse = qml.utils.sparse_hamiltonian(H) +H_sparse = H.sparse_matrix() H_sparse ############################################################################## @@ -264,7 +264,7 @@ def circuit_2(params, excitations, gates_select, params_select): params = np.zeros(len(excitations), requires_grad=True) -@qml.qnode(dev, diff_method="parameter-shift") +@qml.qnode(dev, diff_method="parameter-shift", interface="autograd") def circuit(params): qml.BasisState(hf_state, wires=range(qubits)) diff --git a/demonstrations/tutorial_backprop.py b/demonstrations/tutorial_backprop.py index 285df1d23e..2198861a50 100644 --- a/demonstrations/tutorial_backprop.py +++ b/demonstrations/tutorial_backprop.py @@ -64,7 +64,7 @@ # create a device to execute the circuit on dev = qml.device("default.qubit", wires=3) -@qml.qnode(dev, diff_method="parameter-shift") +@qml.qnode(dev, diff_method="parameter-shift", interface="autograd") def circuit(params): qml.RX(params[0], wires=0) qml.RY(params[1], wires=1) @@ -167,7 +167,7 @@ def parameter_shift(qnode, params): dev = qml.device("default.qubit", wires=4) -@qml.qnode(dev, diff_method="parameter-shift") +@qml.qnode(dev, diff_method="parameter-shift", interface="autograd") def circuit(params): qml.StronglyEntanglingLayers(params, wires=[0, 1, 2, 3]) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1) @ qml.PauliZ(2) @ qml.PauliZ(3)) @@ -264,7 +264,7 @@ def circuit(params): # mode* for the ``default.qubit`` device. -@qml.qnode(dev, diff_method="backprop") +@qml.qnode(dev, diff_method="backprop", interface="autograd") def circuit(params): qml.StronglyEntanglingLayers(params, wires=[0, 1, 2, 3]) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1) @ qml.PauliZ(2) @ qml.PauliZ(3)) @@ -336,8 +336,8 @@ def circuit(params): # forward pass timing # =================== - qnode_shift = qml.QNode(circuit, dev, diff_method="parameter-shift") - qnode_backprop = qml.QNode(circuit, dev, diff_method="backprop") + qnode_shift = qml.QNode(circuit, dev, diff_method="parameter-shift", interface="autograd") + qnode_backprop = qml.QNode(circuit, dev, diff_method="backprop", interface="autograd") # parameter-shift t = timeit.repeat("qnode_shift(params)", globals=globals(), number=num, repeat=reps) @@ -353,8 +353,8 @@ def circuit(params): # Gradient timing # =============== - qnode_shift = qml.QNode(circuit, dev, diff_method="parameter-shift") - qnode_backprop = qml.QNode(circuit, dev, diff_method="backprop") + qnode_shift = qml.QNode(circuit, dev, diff_method="parameter-shift", interface="autograd") + qnode_backprop = qml.QNode(circuit, dev, diff_method="backprop", interface="autograd") # parameter-shift t = timeit.repeat("qml.grad(qnode_shift)(params)", globals=globals(), number=num, repeat=reps) diff --git a/demonstrations/tutorial_barren_plateaus.py b/demonstrations/tutorial_barren_plateaus.py index f5209dd09e..876e18dfff 100644 --- a/demonstrations/tutorial_barren_plateaus.py +++ b/demonstrations/tutorial_barren_plateaus.py @@ -132,7 +132,7 @@ def rand_circuit(params, random_gate_sequence=None, num_qubits=None): for i in range(num_samples): gate_sequence = {i: np.random.choice(gate_set) for i in range(num_qubits)} - qcircuit = qml.QNode(rand_circuit, dev) + qcircuit = qml.QNode(rand_circuit, dev, interface="autograd") grad = qml.grad(qcircuit, argnum=0) params = np.random.uniform(0, 2 * np.pi, size=num_qubits) gradient = grad(params, random_gate_sequence=gate_sequence, num_qubits=num_qubits) @@ -161,7 +161,7 @@ def rand_circuit(params, random_gate_sequence=None, num_qubits=None): grad_vals = [] for i in range(num_samples): dev = qml.device("default.qubit", wires=num_qubits) - qcircuit = qml.QNode(rand_circuit, dev) + qcircuit = qml.QNode(rand_circuit, dev, interface="autograd") grad = qml.grad(qcircuit, argnum=0) gate_set = [qml.RX, qml.RY, qml.RZ] diff --git a/demonstrations/tutorial_classical_kernels.py b/demonstrations/tutorial_classical_kernels.py index 7b73263966..cecc229c68 100644 --- a/demonstrations/tutorial_classical_kernels.py +++ b/demonstrations/tutorial_classical_kernels.py @@ -416,7 +416,7 @@ def ansatz(x1, x2, thetas, amplitudes, wires): ############################################################################### # Next, we construct the quantum node: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def QK_circuit(x1, x2, thetas, amplitudes): ansatz(x1, x2, thetas, amplitudes, wires = range(n_wires)) return qml.probs(wires = range(n_wires)) diff --git a/demonstrations/tutorial_classically_boosted_vqe.py b/demonstrations/tutorial_classically_boosted_vqe.py index 0621a66170..0bd1707812 100644 --- a/demonstrations/tutorial_classically_boosted_vqe.py +++ b/demonstrations/tutorial_classically_boosted_vqe.py @@ -154,7 +154,7 @@ def circuit_VQE(theta, wires): # dev = qml.device('default.qubit', wires=qubits) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(theta): circuit_VQE(theta,range(qubits)) return qml.expval(H) @@ -424,7 +424,7 @@ def cost_fn(theta): wires = range(qubits + 1) dev = qml.device("default.qubit", wires=wires) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def hadamard_test(Uq, Ucl, component='real'): if component == 'imag': diff --git a/demonstrations/tutorial_coherent_vqls.py b/demonstrations/tutorial_coherent_vqls.py index 83f4d9672f..2001971d0f 100644 --- a/demonstrations/tutorial_coherent_vqls.py +++ b/demonstrations/tutorial_coherent_vqls.py @@ -361,7 +361,7 @@ def full_circuit(weights): dev = qml.device("default.qubit", wires=tot_qubits) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def global_ground(weights): # Circuit gates full_circuit(weights) @@ -370,7 +370,7 @@ def global_ground(weights): P[0, 0] = 1.0 return qml.expval(qml.Hermitian(P, wires=range(tot_qubits))) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def ancilla_ground(weights): # Circuit gates full_circuit(weights) @@ -489,7 +489,7 @@ def cost(weights): dev_x = qml.device("default.qubit", wires=n_qubits, shots=n_shots) -@qml.qnode(dev_x) +@qml.qnode(dev_x, interface="autograd") def prepare_and_sample(weights): # Variational circuit generating a guess for the solution vector |x> diff --git a/demonstrations/tutorial_data_reuploading_classifier.py b/demonstrations/tutorial_data_reuploading_classifier.py index 3e5b9a4357..7d1ae371cc 100644 --- a/demonstrations/tutorial_data_reuploading_classifier.py +++ b/demonstrations/tutorial_data_reuploading_classifier.py @@ -262,7 +262,7 @@ def density_matrix(state): # Install any pennylane-plugin to run on some particular backend -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def qcircuit(params, x, y): """A variational quantum circuit representing the Universal classifier. diff --git a/demonstrations/tutorial_diffable_shadows.py b/demonstrations/tutorial_diffable_shadows.py index 9653b19f8b..28861db562 100644 --- a/demonstrations/tutorial_diffable_shadows.py +++ b/demonstrations/tutorial_diffable_shadows.py @@ -105,7 +105,7 @@ H = qml.Hamiltonian([1., 1.], [qml.PauliZ(0) @ qml.PauliZ(1), qml.PauliX(0) @ qml.PauliX(1)]) dev = qml.device("default.qubit", wires=range(2), shots=10000) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def qnode(x, H): qml.Hadamard(0) qml.CNOT((0,1)) @@ -132,7 +132,7 @@ def qnode(x, H): # class methods. dev = qml.device("default.qubit", wires=range(2), shots=1000) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def qnode(x): qml.Hadamard(0) qml.CNOT((0,1)) @@ -196,7 +196,7 @@ def circuit(): obs = qml.PauliX(0) @ qml.PauliZ(3) @ qml.PauliX(6) @ qml.PauliZ(7) dev_ideal = qml.device("default.qubit", wires=range(n_wires), shots=None) -@qml.qnode(dev_ideal) +@qml.qnode(dev_ideal, interface="autograd") def qnode_ideal(): circuit() return qml.expval(obs) @@ -214,12 +214,12 @@ def qnode_ideal(): # repeating experiment 10 times to obtain averages and standard deviations dev = qml.device("default.qubit", wires=range(10), shots=shots) - @qml.qnode(dev) + @qml.qnode(dev, interface="autograd") def qnode_finite(): circuit() return qml.expval(obs) - @qml.qnode(dev) + @qml.qnode(dev, interface="autograd") def qnode_shadow(): circuit() return qml.shadow_expval(obs) @@ -297,7 +297,7 @@ def circuit(): for i in range(n): qml.CNOT((i, (i+1)%n)) -@qml.qnode(dev_ideal) +@qml.qnode(dev_ideal, interface="autograd") def qnode_ideal(): circuit() return qml.expval(H) @@ -311,7 +311,7 @@ def qnode_ideal(): coeffs = np.random.rand(len(all_observables)) H = qml.Hamiltonian(coeffs, all_observables, grouping_type="qwc") - @qml.qnode(dev_ideal) + @qml.qnode(dev_ideal, interface="autograd") def qnode_ideal(): circuit() return qml.expval(H) @@ -321,13 +321,13 @@ def qnode_ideal(): for _ in range(10): dev = qml.device("default.qubit", wires=range(5), shots=shots) - @qml.qnode(dev) + @qml.qnode(dev, interface="autograd") def qnode_finite(): circuit() return qml.expval(H) dev = qml.device("default.qubit", wires=range(5), shots=shots*n_groups) - @qml.qnode(dev) + @qml.qnode(dev, interface="autograd") def qnode_shadow(): circuit() return qml.shadow_expval(H) @@ -421,7 +421,7 @@ def circuit(): # execute qwc measurements dev_finite = qml.device("default.qubit", wires=range(n_wires), shots=int(shots)) - @qml.qnode(dev_finite) + @qml.qnode(dev_finite, interface="autograd") def qnode_finite(H): circuit() return qml.expval(H) @@ -431,7 +431,7 @@ def qnode_finite(H): # execute shadows measurements dev_shadow = qml.device("default.qubit", wires=range(n_wires), shots=int(shots)*n_groups) - @qml.qnode(dev_shadow) + @qml.qnode(dev_shadow, interface="autograd") def qnode(): circuit() return classical_shadow(wires=range(n_wires)) diff --git a/demonstrations/tutorial_differentiable_HF.py b/demonstrations/tutorial_differentiable_HF.py index 512a4f82c7..fc147f43b4 100644 --- a/demonstrations/tutorial_differentiable_HF.py +++ b/demonstrations/tutorial_differentiable_HF.py @@ -267,7 +267,7 @@ dev = qml.device("default.qubit", wires=4) def energy(mol): - @qml.qnode(dev) + @qml.qnode(dev, interface="autograd") def circuit(*args): qml.BasisState(np.array([1, 1, 0, 0]), wires=range(4)) qml.DoubleExcitation(*args[0][0], wires=[0, 1, 2, 3]) diff --git a/demonstrations/tutorial_doubly_stochastic.py b/demonstrations/tutorial_doubly_stochastic.py index 3153900461..df31247a34 100644 --- a/demonstrations/tutorial_doubly_stochastic.py +++ b/demonstrations/tutorial_doubly_stochastic.py @@ -151,8 +151,8 @@ def circuit(params): # Now, we create three QNodes, each corresponding to a device above, # and optimize them using gradient descent via the parameter-shift rule. -qnode_analytic = qml.QNode(circuit, dev_analytic) -qnode_stochastic = qml.QNode(circuit, dev_stochastic) +qnode_analytic = qml.QNode(circuit, dev_analytic, interface="autograd") +qnode_stochastic = qml.QNode(circuit, dev_stochastic, interface="autograd") param_shape = StronglyEntanglingLayers.shape(n_layers=num_layers, n_wires=num_wires) init_params = np.random.uniform(low=0, high=2*np.pi, size=param_shape, requires_grad=True) @@ -293,7 +293,7 @@ def circuit(params): ) -@qml.qnode(dev_stochastic) +@qml.qnode(dev_stochastic, interface="autograd") def circuit(params, n=None): StronglyEntanglingLayers(weights=params, wires=[0, 1]) idx = np.random.choice(np.arange(5), size=n, replace=False) diff --git a/demonstrations/tutorial_expressivity_fourier_series.py b/demonstrations/tutorial_expressivity_fourier_series.py index 4614bff616..be09145d18 100644 --- a/demonstrations/tutorial_expressivity_fourier_series.py +++ b/demonstrations/tutorial_expressivity_fourier_series.py @@ -284,7 +284,7 @@ def W(theta): qml.Rot(theta[0], theta[1], theta[2], wires=0) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def serial_quantum_model(weights, x): for theta in weights[:-1]: @@ -489,7 +489,7 @@ def cost(weights, x, y): dev = qml.device('default.qubit', wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def ansatz(weights): StronglyEntanglingLayers(weights, wires=range(n_qubits)) return qml.expval(qml.Identity(wires=0)) @@ -517,7 +517,7 @@ def W(theta): StronglyEntanglingLayers(theta, wires=range(r)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def parallel_quantum_model(weights, x): W(weights[0]) @@ -675,7 +675,7 @@ def W(theta): BasicEntanglerLayers(theta, wires=range(n_qubits)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def quantum_model(weights, x): W(weights[0]) diff --git a/demonstrations/tutorial_falqon.py b/demonstrations/tutorial_falqon.py index 6962dcb415..b913530f8e 100644 --- a/demonstrations/tutorial_falqon.py +++ b/demonstrations/tutorial_falqon.py @@ -250,7 +250,7 @@ def expval_circuit(beta, measurement_h): def max_clique_falqon(graph, n, beta_1, delta_t, dev): comm_h = build_hamiltonian(graph) # Builds the commutator cost_h, driver_h = qaoa.max_clique(graph, constrained=False) # Builds H_c and H_d - cost_fn = qml.QNode(expval_circuit, dev) # The ansatz + measurement circuit is executable + cost_fn = qml.QNode(expval_circuit, dev, interface="autograd") # The ansatz + measurement circuit is executable beta = [beta_1] # Records each value of beta_k energies = [] # Records the value of the cost function at each step @@ -296,7 +296,7 @@ def max_clique_falqon(graph, n, beta_1, delta_t, dev): # we can create a graph showing the probability of measuring each possible bit string. # We define the following circuit, feeding in the optimal values of :math:`\beta_k`: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def prob_circuit(): ansatz = build_maxclique_ansatz(cost_h, driver_h, delta_t) ansatz(res_beta) @@ -418,7 +418,7 @@ def qaoa_circuit(params, **kwargs): qml.layer(qaoa_layer, depth, params[0], params[1]) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def qaoa_expval(params): qaoa_circuit(params) return qml.expval(cost_h) @@ -450,7 +450,7 @@ def qaoa_expval(params): # define a circuit which outputs the probabilities of measuring each bit string, and # create a bar graph: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def prob_circuit(params): qaoa_circuit(params) return qml.probs(wires=dev.wires) diff --git a/demonstrations/tutorial_givens_rotations.py b/demonstrations/tutorial_givens_rotations.py index 5c1ce99a83..948964e036 100644 --- a/demonstrations/tutorial_givens_rotations.py +++ b/demonstrations/tutorial_givens_rotations.py @@ -189,7 +189,7 @@ dev = qml.device('default.qubit', wires=3) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(x, y): # prepares the reference state |100> qml.BasisState(np.array([1, 0, 0]), wires=[0, 1, 2]) @@ -260,7 +260,7 @@ def circuit(x, y): dev2 = qml.device('default.qubit', wires=6) -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def circuit2(x, y): # prepares reference state qml.BasisState(np.array([1, 1, 1, 0, 0, 0]), wires=[0, 1, 2, 3, 4, 5]) @@ -356,7 +356,7 @@ def circuit2(x, y): dev = qml.device('default.qubit', wires=6) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit3(x, y, z): qml.BasisState(np.array([1, 1, 0, 0, 0, 0]), wires=[i for i in range(6)]) qml.DoubleExcitation(x, wires=[0, 1, 2, 3]) @@ -380,7 +380,7 @@ def circuit3(x, y, z): # above, this time controlling on the state of the first qubit and verify that we can prepare the # desired state. To perform the control, we use the :func:`~.pennylane.ctrl` transform: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit4(x, y, z): qml.BasisState(np.array([1, 1, 0, 0, 0, 0]), wires=[i for i in range(6)]) qml.DoubleExcitation(x, wires=[0, 1, 2, 3]) @@ -438,7 +438,7 @@ def circuit4(x, y, z): dev = qml.device('default.qubit', wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def state_preparation(params): qml.BasisState(np.array([1, 1, 0, 0]), wires=[0, 1, 2, 3]) qml.SingleExcitation(params[0], wires=[1, 2]) diff --git a/demonstrations/tutorial_kernel_based_training.py b/demonstrations/tutorial_kernel_based_training.py index 2a786fe4e2..0c8126c61c 100644 --- a/demonstrations/tutorial_kernel_based_training.py +++ b/demonstrations/tutorial_kernel_based_training.py @@ -254,7 +254,7 @@ projector = np.zeros((2**n_qubits, 2**n_qubits)) projector[0, 0] = 1 -@qml.qnode(dev_kernel) +@qml.qnode(dev_kernel, interface="autograd") def kernel(x1, x2): """The quantum kernel.""" AngleEmbedding(x1, wires=range(n_qubits)) diff --git a/demonstrations/tutorial_kernels_module.py b/demonstrations/tutorial_kernels_module.py index c7d226fc8d..0b90f5c9f7 100644 --- a/demonstrations/tutorial_kernels_module.py +++ b/demonstrations/tutorial_kernels_module.py @@ -292,7 +292,7 @@ def random_params(num_wires, num_layers): # finally extract the probabilities of observing each basis state. -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def kernel_circuit(x1, x2, params): ansatz(x1, params, wires=wires) adjoint_ansatz(x2, params, wires=wires) diff --git a/demonstrations/tutorial_local_cost_functions.py b/demonstrations/tutorial_local_cost_functions.py index 75ade6e3d5..580d64a851 100644 --- a/demonstrations/tutorial_local_cost_functions.py +++ b/demonstrations/tutorial_local_cost_functions.py @@ -124,9 +124,9 @@ def local_cost_simple(rotations): qml.RY(rotations[1][i], wires=i) return [qml.probs(wires=i) for i in range(wires)] -global_circuit = qml.QNode(global_cost_simple, dev) +global_circuit = qml.QNode(global_cost_simple, dev, interface="autograd") -local_circuit = qml.QNode(local_cost_simple, dev) +local_circuit = qml.QNode(local_cost_simple, dev, interface="autograd") def cost_local(rotations): return 1 - np.sum(local_circuit(rotations)[:,0])/wires @@ -252,9 +252,9 @@ def local_cost_simple(rotations): qml.broadcast(qml.CNOT, wires=range(wires), pattern="chain") return qml.probs(wires=[0]) -global_circuit = qml.QNode(global_cost_simple, dev) +global_circuit = qml.QNode(global_cost_simple, dev, interface="autograd") -local_circuit = qml.QNode(local_cost_simple, dev) +local_circuit = qml.QNode(local_cost_simple, dev, interface="autograd") def cost_local(rotations): return 1 - local_circuit(rotations)[0] @@ -344,7 +344,7 @@ def cost_global(rotations): # dev.shots = None -global_circuit = qml.QNode(global_cost_simple, dev) +global_circuit = qml.QNode(global_cost_simple, dev, interface="autograd") print( "Current cost: " + str(cost_global(params_local)) @@ -378,7 +378,7 @@ def cost_tunable(rotations): return 1 - tunable_circuit(rotations)[0] dev.shots = 10000 -tunable_circuit = qml.QNode(tunable_cost_simple, dev) +tunable_circuit = qml.QNode(tunable_cost_simple, dev, interface="autograd") locality = 2 params_tunable = params_local fig, ax = qml.draw_mpl(tunable_circuit, decimals=2)(params_tunable) @@ -441,7 +441,7 @@ def cost_tunable(rotations): wires = 8 dev = qml.device("default.qubit", wires=wires, shots=10000) -global_circuit = qml.QNode(global_cost_simple, dev) +global_circuit = qml.QNode(global_cost_simple, dev, interface="autograd") for runs in range(samples): print("--- New run! ---") @@ -474,7 +474,7 @@ def cost_tunable(rotations): wires = 8 dev = qml.device("default.qubit", wires=wires, shots=10000) -tunable_circuit = qml.QNode(tunable_cost_simple, dev) +tunable_circuit = qml.QNode(tunable_cost_simple, dev, interface="autograd") for runs in range(samples): locality = 1 diff --git a/demonstrations/tutorial_mbqc.py b/demonstrations/tutorial_mbqc.py index b59366e35b..d23e4ca68b 100644 --- a/demonstrations/tutorial_mbqc.py +++ b/demonstrations/tutorial_mbqc.py @@ -97,7 +97,7 @@ dev = qml.device("default.qubit", wires=qubits) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cluster_state(): for node in qubits: qml.Hadamard(wires=[node]) @@ -159,7 +159,7 @@ def cluster_state(): dev = qml.device("default.qubit", wires=2) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def one_bit_teleportation(input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=0) @@ -302,7 +302,7 @@ def generate_random_state(n=1): dev = qml.device("default.qubit", wires=1) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def RZ(theta, input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=0) @@ -323,7 +323,7 @@ def RZ(theta, input_state): mbqc_dev = qml.device("default.qubit", wires=2) -@qml.qnode(mbqc_dev) +@qml.qnode(mbqc_dev, interface="autograd") def RZ_MBQC(theta, input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=0) @@ -364,7 +364,7 @@ def RZ_MBQC(theta, input_state): dev = qml.device("default.qubit", wires=1) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def RX(theta, input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=0) @@ -379,7 +379,7 @@ def RX(theta, input_state): mbqc_dev = qml.device("default.qubit", wires=3) -@qml.qnode(mbqc_dev) +@qml.qnode(mbqc_dev, interface="autograd") def RX_MBQC(theta, input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=0) @@ -444,7 +444,7 @@ def RX_MBQC(theta, input_state): dev = qml.device("default.qubit", wires=2) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def CNOT(input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=[0, 1]) @@ -456,7 +456,7 @@ def CNOT(input_state): mbqc_dev = qml.device("default.qubit", wires=4) -@qml.qnode(mbqc_dev) +@qml.qnode(mbqc_dev, interface="autograd") def CNOT_MBQC(input_state): # Prepare the input state qml.QubitStateVector(input_state, wires=[0, 1]) diff --git a/demonstrations/tutorial_measurement_optimize.py b/demonstrations/tutorial_measurement_optimize.py index cd6310bc29..d5d3f4c0f7 100644 --- a/demonstrations/tutorial_measurement_optimize.py +++ b/demonstrations/tutorial_measurement_optimize.py @@ -141,7 +141,7 @@ ) # generate the cost function -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_circuit(params): ansatz(params, wires=dev.wires) return qml.expval(H) @@ -386,13 +386,13 @@ def cost_circuit(params): dev = qml.device("default.qubit", wires=3) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit1(weights): qml.StronglyEntanglingLayers(weights, wires=range(3)) return qml.expval(obs[0]) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit2(weights): qml.StronglyEntanglingLayers(weights, wires=range(3)) return qml.expval(obs[1]) @@ -407,7 +407,7 @@ def circuit2(weights): # Now, let's use our QWC approach to reduce this down to a *single* measurement # of the probabilities in the shared eigenbasis of both QWC observables: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit_qwc(weights): qml.StronglyEntanglingLayers(weights, wires=range(3)) @@ -453,7 +453,7 @@ def circuit_qwc(weights): # Luckily, PennyLane automatically performs this QWC grouping under the hood. We simply # return the two QWC Pauli terms from the QNode: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(weights): qml.StronglyEntanglingLayers(weights, wires=range(3)) return [ @@ -704,7 +704,7 @@ def format_pauli_word(term): dev = qml.device("default.qubit", wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(weights, group=None, **kwargs): qml.StronglyEntanglingLayers(weights, wires=range(4)) return [qml.expval(o) for o in group] @@ -729,7 +729,7 @@ def circuit(weights, group=None, **kwargs): # automatically optimize the measurements. H = qml.Hamiltonian(coeffs=np.ones(len(terms)), observables=terms, grouping_type="qwc") -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(weights): qml.StronglyEntanglingLayers(weights, wires=range(4)) return qml.expval(H) diff --git a/demonstrations/tutorial_mol_geo_opt.py b/demonstrations/tutorial_mol_geo_opt.py index 76b43f617c..108604dfd7 100644 --- a/demonstrations/tutorial_mol_geo_opt.py +++ b/demonstrations/tutorial_mol_geo_opt.py @@ -195,7 +195,7 @@ def H(x): dev = qml.device("default.qubit", wires=num_wires) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(params, obs, wires): qml.BasisState(hf, wires=wires) qml.DoubleExcitation(params[0], wires=[0, 1, 2, 3]) diff --git a/demonstrations/tutorial_noisy_circuit_optimization.py b/demonstrations/tutorial_noisy_circuit_optimization.py index 879b4967e3..8a8f73cb3b 100644 --- a/demonstrations/tutorial_noisy_circuit_optimization.py +++ b/demonstrations/tutorial_noisy_circuit_optimization.py @@ -94,39 +94,40 @@ B2 = qml.Hermitian(np.array([[1, -1], [-1, -1]]) / np.sqrt(2), wires=1) CHSH_observables = [A1 @ B1, A1 @ B2, A2 @ B1, A2 @ B2] + # subcircuit for creating an entangled pair of qubits def bell_pair(): qml.Hadamard(wires=0) qml.CNOT(wires=[0, 1]) + # circuits for measuring each distinct observable @qml.qnode(dev) def measure_A1B1(): bell_pair() return qml.expval(A1 @ B1) + @qml.qnode(dev) def measure_A1B2(): bell_pair() return qml.expval(A1 @ B2) + @qml.qnode(dev) def measure_A2B1(): bell_pair() return qml.expval(A2 @ B1) + @qml.qnode(dev) def measure_A2B2(): bell_pair() return qml.expval(A2 @ B2) -circuits = qml.QNodeCollection([measure_A1B1, - measure_A1B2, - measure_A2B1, - measure_A2B2]) # now we measure each circuit and construct the CHSH inequality -expvals = circuits() +expvals = [measure_A1B1(), measure_A1B2(), measure_A2B1(), measure_A2B2()] # The CHSH operator is A1 @ B1 + A1 @ B2 + A2 @ B1 - A2 @ B2 CHSH_expval = np.sum(expvals[:3]) - expvals[3] @@ -152,10 +153,11 @@ def measure_A2B2(): # circuit declarations. from pennylane_cirq import ops as cirq_ops + # Note that the 'Operation' op is a generic base class # from PennyLane core. # All other ops are provided by Cirq. -available_ops = [op for op in dir(cirq_ops) if not op.startswith('_')] +available_ops = [op for op in dir(cirq_ops) if not op.startswith("_")] print("\n".join(available_ops)) ############################################################################## @@ -181,23 +183,20 @@ def bell_pair(): qml.CNOT(wires=[0, 1]) cirq_ops.BitFlip(p, wires=0) cirq_ops.BitFlip(p, wires=1) + # measuring the circuits will now use the new noisy bell_pair() function - expvals = circuits() + expvals = [measure_A1B1(), measure_A1B2(), measure_A2B1(), measure_A2B2()] noisy_expvals.append(expvals) noisy_expvals = np.array(noisy_expvals) -CHSH_expvals = np.sum(noisy_expvals[:,:3], axis=1) - noisy_expvals[:,3] +CHSH_expvals = np.sum(noisy_expvals[:, :3], axis=1) - noisy_expvals[:, 3] # Plot the individual observables -plt.plot(noise_vals, noisy_expvals[:, 0], 'D', - label = r"$\hat{A1}\otimes \hat{B1}$", markersize=5) -plt.plot(noise_vals, noisy_expvals[:, 1], 'x', - label = r"$\hat{A1}\otimes \hat{B2}$", markersize=12) -plt.plot(noise_vals, noisy_expvals[:, 2], '+', - label = r"$\hat{A2}\otimes \hat{B1}$", markersize=10) -plt.plot(noise_vals, noisy_expvals[:, 3], 'v', - label = r"$\hat{A2}\otimes \hat{B2}$", markersize=10) -plt.xlabel('Noise parameter') -plt.ylabel(r'Expectation value $\langle \hat{A}_i\otimes\hat{B}_j\rangle$') +plt.plot(noise_vals, noisy_expvals[:, 0], "D", label=r"$\hat{A1}\otimes \hat{B1}$", markersize=5) +plt.plot(noise_vals, noisy_expvals[:, 1], "x", label=r"$\hat{A1}\otimes \hat{B2}$", markersize=12) +plt.plot(noise_vals, noisy_expvals[:, 2], "+", label=r"$\hat{A2}\otimes \hat{B1}$", markersize=10) +plt.plot(noise_vals, noisy_expvals[:, 3], "v", label=r"$\hat{A2}\otimes \hat{B2}$", markersize=10) +plt.xlabel("Noise parameter") +plt.ylabel(r"Expectation value $\langle \hat{A}_i\otimes\hat{B}_j\rangle$") plt.legend() plt.show() @@ -223,10 +222,9 @@ def bell_pair(): # values greater than 2 can safely be considered "quantum". plt.plot(noise_vals, CHSH_expvals, label="CHSH") -plt.plot(noise_vals, 2 * np.ones_like(noise_vals), - label="Quantum-classical boundary") -plt.xlabel('Noise parameter') -plt.ylabel('CHSH Expectation value') +plt.plot(noise_vals, 2 * np.ones_like(noise_vals), label="Quantum-classical boundary") +plt.xlabel("Noise parameter") +plt.ylabel("CHSH Expectation value") plt.legend() plt.show() @@ -260,6 +258,7 @@ def bell_pair(): # technique for modelling noise, but may not be appropriate for all # situations. + @qml.qnode(dev) def circuit(gate_params, noise_param=0.0): qml.RX(gate_params[0], wires=0) @@ -267,6 +266,7 @@ def circuit(gate_params, noise_param=0.0): cirq_ops.Depolarize(noise_param, wires=0) return qml.expval(qml.PauliZ(0)) + gate_pars = [0.54, 0.12] print("Expectation value:", circuit(gate_pars)) @@ -284,12 +284,12 @@ def circuit(gate_params, noise_param=0.0): # :class:`~pennylane.ops.PauliZ` expectation value changes as # a function of the noise strength. -noise_vals = np.linspace(0., 1., 20) +noise_vals = np.linspace(0.0, 1.0, 20) expvals = [circuit(gate_pars, noise_param=p) for p in noise_vals] plt.plot(noise_vals, expvals, label="Expectation value") -plt.plot(noise_vals, np.ones_like(noise_vals), '--', label="Highest possible") -plt.plot(noise_vals, -np.ones_like(noise_vals), '--', label="Lowest possible") +plt.plot(noise_vals, np.ones_like(noise_vals), "--", label="Highest possible") +plt.plot(noise_vals, -np.ones_like(noise_vals), "--", label="Lowest possible") plt.ylabel(r"Expectation value $\langle \hat{Z} \rangle$") plt.xlabel(r"Noise strength $p$") plt.legend() @@ -302,13 +302,16 @@ def circuit(gate_params, noise_param=0.0): # i.e., to tune the qubit state until it has a ``PauliZ`` expectation value # of :math:`-1` (the lowest possible). + # declare the cost functions to be optimized def cost(x): return circuit(x, noise_param=0.0) + def noisy_cost(x): return circuit(x, noise_param=0.3) + # initialize the optimizer opt = qml.GradientDescentOptimizer(stepsize=0.4) @@ -326,10 +329,11 @@ def noisy_cost(x): noisy_circuit_params = opt.step(noisy_cost, noisy_circuit_params) if (i + 1) % 5 == 0: - print("Step {:5d}. Cost: {: .7f}; Noisy Cost: {: .7f}". - format(i + 1, - cost(params), - noisy_cost(noisy_circuit_params))) + print( + "Step {:5d}. Cost: {: .7f}; Noisy Cost: {: .7f}".format( + i + 1, cost(params), noisy_cost(noisy_circuit_params) + ) + ) print("\nOptimized rotation angles (noise-free case):") print("({: .7f}, {: .7f})".format(*params)) @@ -452,17 +456,18 @@ def noisy_cost(x): angles = np.linspace(0, 2 * np.pi, 50) theta2 = np.pi / 4 + def param_shift(theta1): - return 0.5 * (noisy_cost([theta1 + np.pi / 2, theta2]) - \ - noisy_cost([theta1 - np.pi / 2, theta2])) + return 0.5 * ( + noisy_cost([theta1 + np.pi / 2, theta2]) - noisy_cost([theta1 - np.pi / 2, theta2]) + ) + noisy_expvals = [noisy_cost([theta1, theta2]) for theta1 in angles] noisy_param_shift = [param_shift(theta1) for theta1 in angles] -plt.plot(angles, noisy_expvals, - label="Expectation value") # looks like 0.4 * cos(phi) -plt.plot(angles, noisy_param_shift, - label="Parameter-shift value") # looks like -0.4 * sin(phi) +plt.plot(angles, noisy_expvals, label="Expectation value") # looks like 0.4 * cos(phi) +plt.plot(angles, noisy_param_shift, label="Parameter-shift value") # looks like -0.4 * sin(phi) plt.ylabel(r"Expectation value $\langle \hat{Z} \rangle$") plt.xlabel(r"Angle $\theta_1$") plt.legend() @@ -538,4 +543,4 @@ def param_shift(theta1): # # About the author # ---------------- -# .. include:: ../_static/authors/nathan_killoran.txt \ No newline at end of file +# .. include:: ../_static/authors/nathan_killoran.txt diff --git a/demonstrations/tutorial_plugins_hybrid.py b/demonstrations/tutorial_plugins_hybrid.py index f7a24efffa..8ebc31d08f 100644 --- a/demonstrations/tutorial_plugins_hybrid.py +++ b/demonstrations/tutorial_plugins_hybrid.py @@ -260,7 +260,7 @@ def cost(params): dev_fock = qml.device("strawberryfields.fock", wires=2, cutoff_dim=10) -@qml.qnode(dev_qubit) +@qml.qnode(dev_qubit, interface="autograd") def qubit_rotation(phi1, phi2): """Qubit rotation QNode""" qml.RX(phi1, wires=0) diff --git a/demonstrations/tutorial_qgrnn.py b/demonstrations/tutorial_qgrnn.py index 695ff344af..a88df5799d 100644 --- a/demonstrations/tutorial_qgrnn.py +++ b/demonstrations/tutorial_qgrnn.py @@ -557,7 +557,7 @@ def cost_function(weight_params, bias_params): qgrnn_dev = qml.device("default.qubit", wires=2 * qubit_number + 1) # Defines the new QNode -qgrnn_qnode = qml.QNode(qgrnn, qgrnn_dev) +qgrnn_qnode = qml.QNode(qgrnn, qgrnn_dev, interface="autograd") steps = 300 diff --git a/demonstrations/tutorial_quantum_natural_gradient.py b/demonstrations/tutorial_quantum_natural_gradient.py index 2e7e6f053c..22f8b7fd21 100644 --- a/demonstrations/tutorial_quantum_natural_gradient.py +++ b/demonstrations/tutorial_quantum_natural_gradient.py @@ -192,7 +192,7 @@ dev = qml.device("default.qubit", wires=3) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(params): # |psi_0>: state preparation qml.RY(np.pi / 4, wires=0) @@ -273,7 +273,7 @@ def layer0_subcircuit(params): # We can see that the diagonal terms are simply given by the variance: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer0_diag(params): layer0_subcircuit(params) return qml.var(qml.PauliZ(0)), qml.var(qml.PauliZ(1)) @@ -289,13 +289,13 @@ def layer0_diag(params): # off-diagonal covariance terms of :math:`g^{(0)}`: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer0_off_diag_single(params): layer0_subcircuit(params) return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer0_off_diag_double(params): layer0_subcircuit(params) ZZ = np.kron(np.diag([1, -1]), np.diag([1, -1])) @@ -350,7 +350,7 @@ def layer1_subcircuit(params): # :target: javascript:void(0) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer1_diag(params): layer1_subcircuit(params) return qml.var(qml.PauliY(1)), qml.var(qml.PauliX(2)) @@ -369,13 +369,13 @@ def layer1_diag(params): # observables to be computed. -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer1_off_diag_single(params): layer1_subcircuit(params) return qml.expval(qml.PauliY(1)), qml.expval(qml.PauliX(2)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def layer1_off_diag_double(params): layer1_subcircuit(params) X = np.array([[0, 1], [1, 0]]) diff --git a/demonstrations/tutorial_quanvolution.py b/demonstrations/tutorial_quanvolution.py index 465d261a78..43a3ea9c11 100644 --- a/demonstrations/tutorial_quanvolution.py +++ b/demonstrations/tutorial_quanvolution.py @@ -145,7 +145,7 @@ # Random circuit parameters rand_params = np.random.uniform(high=2 * np.pi, size=(n_layers, 4)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(phi): # Encoding of 4 classical input values for j in range(4): diff --git a/demonstrations/tutorial_qubit_rotation.py b/demonstrations/tutorial_qubit_rotation.py index b8de2d943d..acc9ab0b19 100644 --- a/demonstrations/tutorial_qubit_rotation.py +++ b/demonstrations/tutorial_qubit_rotation.py @@ -214,7 +214,7 @@ def circuit(params): # **directly above** the function definition: -@qml.qnode(dev1) +@qml.qnode(dev1, interface="autograd") def circuit(params): qml.RX(params[0], wires=0) qml.RY(params[1], wires=0) @@ -266,7 +266,7 @@ def circuit(params): # two positional arguments, instead of one array argument: -@qml.qnode(dev1) +@qml.qnode(dev1, interface="autograd") def circuit2(phi1, phi2): qml.RX(phi1, wires=0) qml.RY(phi2, wires=0) diff --git a/demonstrations/tutorial_qubit_tapering.py b/demonstrations/tutorial_qubit_tapering.py index 1036c75866..5a77fe3458 100644 --- a/demonstrations/tutorial_qubit_tapering.py +++ b/demonstrations/tutorial_qubit_tapering.py @@ -177,8 +177,8 @@ # representation of Hamiltonians. This allows us to directly diagonalize them to obtain exact values # of the ground-state energies. -H_sparse = qml.SparseHamiltonian(qml.utils.sparse_hamiltonian(H), wires=H.wires) -H_tapered_sparse = qml.SparseHamiltonian(qml.utils.sparse_hamiltonian(H_tapered), wires=H_tapered.wires) +H_sparse = qml.SparseHamiltonian(H.sparse_matrix(), wires=H.wires) +H_tapered_sparse = qml.SparseHamiltonian(H_tapered.sparse_matrix(), wires=H_tapered.wires) print("Eigenvalues of H:\n", qml.eigvals(H_sparse, k=16)) print("\nEigenvalues of H_tapered:\n", qml.eigvals(H_tapered_sparse, k=4)) @@ -202,23 +202,23 @@ # Hartree-Fock energies for each Hamiltonian. dev = qml.device("default.qubit", wires=H.wires) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(): qml.BasisState(np.array([1, 1, 0, 0]), wires=H.wires) return qml.state() qubit_state = circuit() -HF_energy = qubit_state.T @ qml.utils.sparse_hamiltonian(H).toarray() @ qubit_state +HF_energy = qubit_state.T @ H.sparse_matrix().toarray() @ qubit_state print(f"HF energy: {np.real(HF_energy):.8f} Ha") dev = qml.device("default.qubit", wires=H_tapered.wires) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(): qml.BasisState(np.array([1, 1]), wires=H_tapered.wires) return qml.state() qubit_state = circuit() -HF_energy = qubit_state.T @ qml.utils.sparse_hamiltonian(H_tapered).toarray() @ qubit_state +HF_energy = qubit_state.T @ H_tapered.sparse_matrix().toarray() @ qubit_state print(f"HF energy (tapered): {np.real(HF_energy):.8f} Ha") ############################################################################## @@ -245,7 +245,7 @@ def circuit(): ] dev = qml.device("default.qubit", wires=H_tapered.wires) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def tapered_circuit(params): qml.BasisState(state_tapered, wires=H_tapered.wires) for idx, tapered_op in enumerate(tapered_doubles + tapered_singles): diff --git a/demonstrations/tutorial_rosalin.py b/demonstrations/tutorial_rosalin.py index 57a0d94e18..13368b6626 100644 --- a/demonstrations/tutorial_rosalin.py +++ b/demonstrations/tutorial_rosalin.py @@ -186,7 +186,7 @@ from pennylane.templates.layers import StronglyEntanglingLayers -@qml.qnode(non_analytic_dev, diff_method="parameter-shift") +@qml.qnode(non_analytic_dev, diff_method="parameter-shift", interface="autograd") def qnode(weights, observable): StronglyEntanglingLayers(weights, wires=non_analytic_dev.wires) return qml.expval(observable) @@ -235,7 +235,7 @@ def cost(params): # Here, we will split the 8000 total shots evenly across all Hamiltonian terms, # also known as *uniform deterministic sampling*. -@qml.qnode(non_analytic_dev, diff_method="parameter-shift") +@qml.qnode(non_analytic_dev, diff_method="parameter-shift", interface="autograd") def qnode(weights, obs): StronglyEntanglingLayers(weights, wires=non_analytic_dev.wires) return qml.expval(obs) @@ -448,7 +448,7 @@ def estimate_hamiltonian(self, params, shots): results = [] - @qml.qnode(rosalin_device, diff_method="parameter-shift") + @qml.qnode(rosalin_device, diff_method="parameter-shift", interface="autograd") def qnode(weights, observable): StronglyEntanglingLayers(weights, wires=rosalin_device.wires) return qml.sample(observable) @@ -555,7 +555,7 @@ def step(self, params): # also create a separate cost function using an 'exact' quantum device, so that we can keep track of the # *exact* cost function value at each iteration. -@qml.qnode(analytic_dev) +@qml.qnode(analytic_dev, interface="autograd") def cost_analytic(weights): StronglyEntanglingLayers(weights, wires=analytic_dev.wires) return qml.expval(qml.Hamiltonian(coeffs, obs)) @@ -593,7 +593,7 @@ def cost_analytic(weights): non_analytic_dev.shots = adam_shots_per_eval -@qml.qnode(non_analytic_dev, diff_method="parameter-shift") +@qml.qnode(non_analytic_dev, diff_method="parameter-shift", interface="autograd") def cost(weights): StronglyEntanglingLayers(weights, wires=non_analytic_dev.wires) return qml.expval(qml.Hamiltonian(coeffs, obs)) diff --git a/demonstrations/tutorial_sc_qubits.py b/demonstrations/tutorial_sc_qubits.py index 65b11d26d5..973fc1aa67 100644 --- a/demonstrations/tutorial_sc_qubits.py +++ b/demonstrations/tutorial_sc_qubits.py @@ -416,14 +416,14 @@ # Implement displacement and rotation and measure both X and P observables -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def measure_P_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) return qml.sample(qml.P(0)) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def measure_X_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) @@ -499,7 +499,7 @@ def measure_X_shots(time, state): dev2 = qml.device("default.qubit", wires=1) # Implement Hamiltonian evolution given phase phi and time t, from a given initial state -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def H_evolve(state, phi, time): if state == 1: @@ -513,7 +513,7 @@ def H_evolve(state, phi, time): # Implement X rotation exactly -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def Sc_X_rot(state, phi): if state == 1: @@ -524,7 +524,7 @@ def Sc_X_rot(state, phi): # Implement Y rotation exactly -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def Sc_Y_rot(state, phi): if state == 1: @@ -617,7 +617,7 @@ def Sc_Y_rot(state, phi): Two_qubit_H = qml.Hamiltonian(coeffs, ops) # Implement Hamiltonian evolution for time t and some initial computational basis state -@qml.qnode(dev3) +@qml.qnode(dev3, interface="autograd") def Sc_ISWAP(basis_state, time): qml.templates.BasisStatePreparation(basis_state, wires=range(2)) ApproxTimeEvolution(Two_qubit_H, time, 1) @@ -625,7 +625,7 @@ def Sc_ISWAP(basis_state, time): # Implement ISWAP exactly -@qml.qnode(dev3) +@qml.qnode(dev3, interface="autograd") def iswap(basis_state): qml.templates.BasisStatePreparation(basis_state, wires=range(2)) qml.ISWAP(wires=[0, 1]) @@ -732,7 +732,7 @@ def cnot_with_iswap(): # the evolution under this Hamiltonian for a time :math:`t=\tfrac{\pi}{4\Omega}` with :math:`R_x` and :math:`R_y` rotations # and a ``qml.Hadamard`` gate: # -@qml.qnode(dev3) +@qml.qnode(dev3, interface="autograd") def H_evolve(state, phi, time): # Prepare initial state qml.templates.BasisStatePreparation(state, wires=range(2)) diff --git a/demonstrations/tutorial_testing_symmetry.py b/demonstrations/tutorial_testing_symmetry.py index a1bf46471b..9c858dd0e3 100644 --- a/demonstrations/tutorial_testing_symmetry.py +++ b/demonstrations/tutorial_testing_symmetry.py @@ -322,7 +322,7 @@ def CU_cpy(): # # Circuit for average symmetry -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def avg_symm(hamiltonian, time): # Use Choi-Jamiołkowski isomorphism diff --git a/demonstrations/tutorial_tn_circuits.py b/demonstrations/tutorial_tn_circuits.py index 3bb5e7c3b1..f91b2999f4 100644 --- a/demonstrations/tutorial_tn_circuits.py +++ b/demonstrations/tutorial_tn_circuits.py @@ -138,7 +138,7 @@ def block(weights, wires): dev = qml.device("default.qubit", wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(template_weights): qml.MPS( wires=range(4), @@ -173,7 +173,7 @@ def deep_block(weights, wires): dev = qml.device("default.qubit", wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(template_weights): qml.MPS( wires=range(4), @@ -222,7 +222,7 @@ def wide_block(weights, wires): dev = qml.device("default.qubit", wires=8) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(template_weights): qml.MPS( wires=range(8), @@ -253,7 +253,7 @@ def block(weights, wires): dev = qml.device("default.qubit", wires=8) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(template_weights): qml.TTN( wires=range(8), @@ -330,7 +330,7 @@ def block(weights, wires): dev = qml.device("default.qubit", wires=4) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(image, template_weights): qml.BasisStatePreparation(image, wires=range(4)) qml.TTN( diff --git a/demonstrations/tutorial_trapped_ions.py b/demonstrations/tutorial_trapped_ions.py index fd990fadcf..ee95f54799 100644 --- a/demonstrations/tutorial_trapped_ions.py +++ b/demonstrations/tutorial_trapped_ions.py @@ -422,7 +422,7 @@ def evolution(phi, t): dev = qml.device("default.qubit", wires=1) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def ion_hadamard(state): if state == 1: @@ -440,7 +440,7 @@ def ion_hadamard(state): return qml.state() #For comparison, we use the Hadamard built into PennyLane -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def hadamard(state): if state == 1: @@ -459,7 +459,7 @@ def hadamard(state): # A similar exercise can be done for the :math:`T` gate: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def ion_Tgate(state): if state == 1: @@ -472,7 +472,7 @@ def ion_Tgate(state): return qml.state() -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def tgate(state): if state == 1: @@ -504,7 +504,7 @@ def tgate(state): import matplotlib.pyplot as plt -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def evolution_prob(t): qml.QubitUnitary(evolution(0, t / Omega), wires=0) @@ -811,7 +811,7 @@ def Molmer_Sorensen(t): dev2 = qml.device("default.qubit",wires=2) -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def ion_cnot(basis_state): #Prepare the two-qubit basis states from the input @@ -827,7 +827,7 @@ def ion_cnot(basis_state): return qml.state() #Compare with built-in CNOT -@qml.qnode(dev2) +@qml.qnode(dev2, interface="autograd") def cnot_gate(basis_state): qml.templates.BasisStatePreparation(basis_state, wires=range(2)) diff --git a/demonstrations/tutorial_variational_classifier.py b/demonstrations/tutorial_variational_classifier.py index c4a7ff1059..411a8bcc0f 100644 --- a/demonstrations/tutorial_variational_classifier.py +++ b/demonstrations/tutorial_variational_classifier.py @@ -113,7 +113,7 @@ def statepreparation(x): # we call the parameters ``weights``. -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(weights, x): statepreparation(x) @@ -318,7 +318,7 @@ def statepreparation(a): ang = get_angles(x) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def test(angles): statepreparation(angles) @@ -355,7 +355,7 @@ def layer(W): # layer functions. -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def circuit(weights, angles): statepreparation(angles) diff --git a/demonstrations/tutorial_vqe.py b/demonstrations/tutorial_vqe.py index e8ebad0245..f5065bc8da 100644 --- a/demonstrations/tutorial_vqe.py +++ b/demonstrations/tutorial_vqe.py @@ -146,7 +146,7 @@ def circuit(param, wires): # run the cost function as an executable QNode with the gate parameter :math:`\theta`: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(param): circuit(param, wires=range(qubits)) return qml.expval(H) diff --git a/demonstrations/tutorial_vqe_qng.py b/demonstrations/tutorial_vqe_qng.py index 4eecfdbccb..806a490372 100644 --- a/demonstrations/tutorial_vqe_qng.py +++ b/demonstrations/tutorial_vqe_qng.py @@ -64,7 +64,7 @@ def circuit(params, wires=0): H = qml.Hamiltonian(coeffs, obs) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(params): circuit(params) return qml.expval(H) @@ -297,7 +297,7 @@ def ansatz(params, wires=[0, 1, 2, 3]): # the Hartree-Fock state of the hydrogen molecule described in the minimal basis. # Again, we define the cost function to be the following QNode that measures ``expval(H)``: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost(params): ansatz(params) return qml.expval(hamiltonian) diff --git a/demonstrations/tutorial_vqe_spin_sectors.py b/demonstrations/tutorial_vqe_spin_sectors.py index 04f03f2fc1..9596a81271 100644 --- a/demonstrations/tutorial_vqe_spin_sectors.py +++ b/demonstrations/tutorial_vqe_spin_sectors.py @@ -191,7 +191,7 @@ def circuit(params, wires): # a cost function that can be evaluated with the circuit parameters: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(params): circuit(params, wires=range(qubits)) return qml.expval(H) @@ -202,7 +202,7 @@ def cost_fn(params): # we can now define a function to compute its expectation value: -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def S2_exp_value(params): circuit(params, wires=range(qubits)) return qml.expval(S2) @@ -304,13 +304,13 @@ def circuit(params, wires): # and the total spin operator for the new circuit. -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def cost_fn(params): circuit(params, wires=range(qubits)) return qml.expval(H) -@qml.qnode(dev) +@qml.qnode(dev, interface="autograd") def S2_exp_value(params): circuit(params, wires=range(qubits)) return qml.expval(S2) diff --git a/demonstrations/tutorial_vqls.py b/demonstrations/tutorial_vqls.py index 64d7db0792..d4c3ac1746 100644 --- a/demonstrations/tutorial_vqls.py +++ b/demonstrations/tutorial_vqls.py @@ -280,7 +280,7 @@ def variational_block(weights): dev_mu = qml.device("default.qubit", wires=tot_qubits) -@qml.qnode(dev_mu) +@qml.qnode(dev_mu, interface="autograd") def local_hadamard_test(weights, l=None, lp=None, j=None, part=None): # First Hadamard gate applied to the ancillary qubit. @@ -467,7 +467,7 @@ def cost_loc(weights): dev_x = qml.device("default.qubit", wires=n_qubits, shots=n_shots) -@qml.qnode(dev_x) +@qml.qnode(dev_x, interface="autograd") def prepare_and_sample(weights): # Variational circuit generating a guess for the solution vector |x> diff --git a/demonstrations/tutorial_vqt.py b/demonstrations/tutorial_vqt.py index c6138d962e..d5543fa344 100644 --- a/demonstrations/tutorial_vqt.py +++ b/demonstrations/tutorial_vqt.py @@ -274,7 +274,7 @@ def quantum_circuit(rotation_params, coupling_params, sample=None): # Constructs the QNode -qnode = qml.QNode(quantum_circuit, dev) +qnode = qml.QNode(quantum_circuit, dev, interface="autograd") ###################################################################### From e8811cdfe6d93b63335f6fe06ebbb02e491f48c1 Mon Sep 17 00:00:00 2001 From: Romain Moyard Date: Thu, 23 Feb 2023 16:32:33 -0500 Subject: [PATCH 07/24] Update classical shadows (#708) * More * typo * Trigger CI * lightning req no depth * Trigger CI --- demonstrations/tutorial_classical_shadows.py | 26 ++++++++++++-------- requirements.txt | 1 - requirements_no_deps.txt | 1 + 3 files changed, 17 insertions(+), 11 deletions(-) diff --git a/demonstrations/tutorial_classical_shadows.py b/demonstrations/tutorial_classical_shadows.py index b82544f943..80b7b87d3d 100644 --- a/demonstrations/tutorial_classical_shadows.py +++ b/demonstrations/tutorial_classical_shadows.py @@ -198,6 +198,7 @@ def calculate_classical_shadow(circuit_template, params, shadow_size, num_qubits # set up a two-qubit device with shots = 1 to ensure that we only get a single measurement dev = qml.device("default.qubit", wires=num_qubits, shots=1) + # simple circuit to prepare rho @qml.qnode(dev) def local_qubit_rotation_circuit(params, **kwargs): @@ -352,6 +353,7 @@ def shadow_state_reconstruction(shadow): dev = qml.device("default.qubit", wires=num_qubits, shots=1) + # circuit to create a Bell state and measure it in # the bases specified by the 'observable' keyword argument. @qml.qnode(dev) @@ -387,6 +389,7 @@ def bell_state_circuit(params, **kwargs): bell_state = np.array([[0.5, 0, 0, 0.5], [0, 0, 0, 0], [0, 0, 0, 0], [0.5, 0, 0, 0.5]]) + ############################################################################## # To measure the closeness we can use the operator norm. @@ -435,6 +438,7 @@ def operator_2_norm(R): plt.ylabel("Distance") plt.show() + ############################################################################## # As expected, when the number of snapshots increases, the state reconstruction # becomes closer to the ideal state. @@ -520,8 +524,8 @@ def estimate_shadow_obervable(shadow, observable, k=10): # assign the splits temporarily b_lists_k, obs_lists_k = ( - b_lists[i : i + shadow_size // k], - obs_lists[i : i + shadow_size // k], + b_lists[i: i + shadow_size // k], + obs_lists[i: i + shadow_size // k], ) # find the exact matches for the observable of interest at the specified locations @@ -581,8 +585,7 @@ def shadow_bound(error, observables, failure_rate=0.01): dev = qml.device("default.qubit", wires=num_qubits, shots=1) -@qml.qnode(dev) -def circuit(params, **kwargs): +def circuit_base(params, **kwargs): observables = kwargs.pop("observable") for w in range(num_qubits): qml.Hadamard(wires=w) @@ -594,6 +597,8 @@ def circuit(params, **kwargs): return [qml.expval(o) for o in observables] +circuit = qml.QNode(circuit_base, dev) + params = np.random.randn(2 * num_qubits) ############################################################################## @@ -604,9 +609,9 @@ def circuit(params, **kwargs): # O = \sum_{i=0}^{n-1} X_i X_{i+1} + Y_i Y_{i+1} + Z_i Z_{i+1}. list_of_observables = ( - [qml.PauliX(i) @ qml.PauliX(i + 1) for i in range(num_qubits - 1)] - + [qml.PauliY(i) @ qml.PauliY(i + 1) for i in range(num_qubits - 1)] - + [qml.PauliZ(i) @ qml.PauliZ(i + 1) for i in range(num_qubits - 1)] + [qml.PauliX(i) @ qml.PauliX(i + 1) for i in range(num_qubits - 1)] + + [qml.PauliY(i) @ qml.PauliY(i + 1) for i in range(num_qubits - 1)] + + [qml.PauliZ(i) @ qml.PauliZ(i + 1) for i in range(num_qubits - 1)] ) ############################################################################## @@ -652,9 +657,10 @@ def circuit(params, **kwargs): dev_exact = qml.device("default.qubit", wires=num_qubits) # change the simulator to be the exact one. -circuit.device = dev_exact +circuit = qml.QNode(circuit_base, dev_exact) + expval_exact = [ - circuit(params, wires=dev_exact.wires, observable=[o]) for o in list_of_observables + circuit(params, observable=[o]) for o in list_of_observables ] ############################################################################## @@ -691,7 +697,7 @@ def circuit(params, **kwargs): # calculate entanglement witnesses, and even find a way to approximate the von Neumann entropy. # These applications illustrate the potential power # of classical shadows for the characterization of quantum systems. - + ############################################################################## # References diff --git a/requirements.txt b/requirements.txt index cb793fce5c..3fb1c53526 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,7 +16,6 @@ pyscf==2.1.1 cirq-core==0.14.1 cirq-pasqal==0.14.1 openfermionpyscf==0.5 # required by 2 tutorials: tutorial_quantum_chemistry and tutorial_adaptive_circuits and tutorial_diffable_shadows -pennylane-lightning git+https://github.com/PennyLaneAI/pennylane.git git+https://github.com/PennyLaneAI/pennylane-sf.git git+https://github.com/PennyLaneAI/pennylane-cirq.git diff --git a/requirements_no_deps.txt b/requirements_no_deps.txt index fd5d3d3aa6..2cbcb376ed 100644 --- a/requirements_no_deps.txt +++ b/requirements_no_deps.txt @@ -1,2 +1,3 @@ mitiq==0.13.0 pyquil==2.21 +pennylane-lightning \ No newline at end of file From 942cbb57e9373dc0630d19e3a1a94b1071553146 Mon Sep 17 00:00:00 2001 From: David Wierichs Date: Fri, 24 Feb 2023 17:15:46 +0100 Subject: [PATCH 08/24] Update SPSA demo to count circuit executions manually. (#706) * new version of SPSA demo * Update demonstrations/spsa.py Co-authored-by: Josh Izaac * simplify code, update outputs and figures * Apply suggestions from code review Co-authored-by: Romain Moyard * review and tweaking * make spsa an executing tutorial * move file * fix links * fix some more links * author bio * line breaks, undo link change, fixing them actually * links... * links again... bio * update to count executions manually * reword device executions -> circuit executions. avoid recreating device and qnode * print executions --------- Co-authored-by: Josh Izaac Co-authored-by: Romain Moyard --- demonstrations/tutorial_spsa.py | 106 +++++++++++++++----------------- 1 file changed, 49 insertions(+), 57 deletions(-) diff --git a/demonstrations/tutorial_spsa.py b/demonstrations/tutorial_spsa.py index d4dcd6dee3..3c313a49df 100644 --- a/demonstrations/tutorial_spsa.py +++ b/demonstrations/tutorial_spsa.py @@ -14,7 +14,7 @@ tutorial_vqe_qng Accelerating VQEs with quantum natural gradient qnspsa Quantum natural SPSA optimizer -*Authors: Antal Szava & David Wierichs — Posted: 19 March 2021. Last updated: 10 February 2023.* +*Authors: Antal Szava & David Wierichs — Posted: 19 March 2021. Last updated: 23 February 2023.* In this tutorial, we investigate using a stochastic optimizer called the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to optimize quantum @@ -29,7 +29,7 @@ 2. The variational quantum eigensolver on a simulated hardware device. Throughout the demo, we show results obtained with SPSA and with gradient -descent and also compare the number of device executions required to complete +descent and also compare the number of executed circuits required to complete each optimization. Background @@ -147,7 +147,7 @@ .. note:: - Just as with other PennyLane device, the number of samples taken for a device + Just as with other PennyLane device, the number of samples taken for a circuit execution can be specified using the ``shots`` keyword argument of the device. @@ -191,51 +191,44 @@ def circuit(param): ############################################################################## # We will execute a few optimizations in this demo, so let's prepare a convenience -# function that runs an optimizer instance and records the cost values and the -# number of device executions along the way. The latter will be an interesting -# quantity to evaluate the optimization cost on hardware! +# function that runs an optimizer instance and records the cost values +# along the way. Together with the number of executed circuits, this will be an +# interesting quantity to evaluate the optimization cost on hardware! -def run_optimizer(optimizer, cost_function, init_param, num_steps, interval): +def run_optimizer(opt, cost_function, init_param, num_steps, interval, execs_per_step): # Copy the initial parameters to make sure they are never overwritten param = init_param.copy() # Obtain the device used in the cost function dev = cost_function.device - # Initialize the memory for cost values and device executions during the optimization + # Initialize the memory for cost values during the optimization cost_history = [] - exec_history = [0] # Monitor the initial cost value cost_history.append(cost_function(param)) - execs_per_cost_eval = dev.num_executions + exec_history = [0] - print( - f"\nRunning the {optimizer.__class__.__name__} optimizer for {num_steps} iterations." - ) + print(f"\nRunning the {opt.__class__.__name__} optimizer for {num_steps} iterations.") for step in range(num_steps): - # Perform an update step - param = optimizer.step(cost_function, param) - - # Monitor the device executions, deducting the executions for cost monitoring - exec_history.append(dev.num_executions - (step + 1) * execs_per_cost_eval) - - # Monitor the cost value - cost_history.append(cost_function(param)) - # Print out the status of the optimization if step % interval == 0: print( - f"Iteration = {step:3d}, " - f"Device executions = {exec_history[step]:4d}, " + f"Step {step:3d}: Circuit executions: {exec_history[step]:4d}, " f"Cost = {cost_history[step]}" ) + # Perform an update step + param = opt.step(cost_function, param) + + # Monitor the cost value + cost_history.append(cost_function(param)) + exec_history.append((step + 1) * execs_per_step) + print( - f"Iteration = {num_steps:3d}, Device executions = {exec_history[-1]:4d}, " + f"Step {num_steps:3d}: Circuit executions: {exec_history[-1]:4d}, " f"Cost = {cost_history[-1]}" ) - return cost_history, exec_history @@ -269,32 +262,30 @@ def run_optimizer(optimizer, cost_function, init_param, num_steps, interval): num_steps_spsa = 200 opt = qml.SPSAOptimizer(maxiter=num_steps_spsa, c=0.15, a=0.2) +# We spend 2 circuit evaluations per step: +execs_per_step = 2 cost_history_spsa, exec_history_spsa = run_optimizer( - opt, cost_function, init_param, num_steps_spsa, 20 + opt, cost_function, init_param, num_steps_spsa, 20, execs_per_step ) ############################################################################## # Now let's perform the same optimization using gradient descent. We set the # step size according to a favourable value found after grid search for fast -# convergence. Note that we also create a new device in order to reset the -# execution count to 0. With the new device, we recreate the cost function as well. - -# Create a new device and a qnode as cost function -device = qml.device("qiskit.aer", wires=num_wires, shots=1000) -cost_function = qml.QNode(circuit, device) +# convergence. num_steps_grad = 15 opt = qml.GradientDescentOptimizer(stepsize=0.3) +# We spend 2 circuit evaluations per parameter per step: +execs_per_step = 2 * np.prod(param_shape) cost_history_grad, exec_history_grad = run_optimizer( - opt, cost_function, init_param, num_steps_grad, 3 + opt, cost_function, init_param, num_steps_grad, 3, execs_per_step ) ############################################################################## # SPSA and gradient descent comparison # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# At this point, nothing else remains but to check which of these approaches did -# better! +# At this point, nothing else remains but to check which of these approaches did better! import matplotlib.pyplot as plt plt.figure(figsize=(10, 6)) @@ -302,7 +293,7 @@ def run_optimizer(optimizer, cost_function, init_param, num_steps, interval): plt.plot(exec_history_grad, cost_history_grad, label="Gradient descent") plt.plot(exec_history_spsa, cost_history_spsa, label="SPSA") -plt.xlabel("Device executions", fontsize=14) +plt.xlabel("Circuit executions", fontsize=14) plt.ylabel("Cost function value", fontsize=14) plt.grid() @@ -316,15 +307,15 @@ def run_optimizer(optimizer, cost_function, init_param, num_steps, interval): # compared to gradient descent! # # Let's take a deeper dive to see how much better it actually is by computing -# the ratio of required device executions to reach an absolute accuracy of 0.01. +# the ratio of required circuit executions to reach an absolute accuracy of 0.01. # grad_execs_to_prec = exec_history_grad[np.where(np.array(cost_history_grad) < -0.99)[0][0]] spsa_execs_to_prec = exec_history_spsa[np.where(np.array(cost_history_spsa) < -0.99)[0][0]] -print(f"Device execution ratio: {np.round(grad_execs_to_prec/spsa_execs_to_prec, 3)}.") +print(f"Circuit execution ratio: {np.round(grad_execs_to_prec/spsa_execs_to_prec, 3)}.") ############################################################################## # This means that SPSA found the minimum up to an absolute accuracy of 0.01 while -# using ten times fewer device executions than gradient descent! That's a huge +# using multiple times fewer circuit executions than gradient descent! That's an important # saving, especially when running the algorithm on actual quantum hardware. # # SPSA and the variational quantum eigensolver @@ -398,14 +389,18 @@ def circuit(param): # This random seed was used in the original VQE demo and is known to allow the # gradient descent algorithm to converge to the global minimum. np.random.seed(0) -init_param = np.random.normal(0, np.pi, (2, num_qubits, 3), requires_grad=True) +param_shape = (2, num_qubits, 3) +init_param = np.random.normal(0, np.pi, param_shape, requires_grad=True) # Initialize the optimizer - optimal step size was found through a grid search opt = qml.GradientDescentOptimizer(stepsize=2.2) +# We spend 2 * 15 circuit evaluations per parameter per step, as there are +# 15 Hamiltonian terms +execs_per_step = 2 * 15 * np.prod(param_shape) # Run the optimization cost_history_grad, exec_history_grad = run_optimizer( - opt, cost_function, init_param, num_steps_grad, 3 + opt, cost_function, init_param, num_steps_grad, 3, execs_per_step ) final_energy = cost_history_grad[-1] @@ -426,11 +421,11 @@ def circuit(param): plt.xticks(fontsize=13) plt.yticks(fontsize=13) -plt.xlabel("Device executions", fontsize=14) +plt.xlabel("Circuit executions", fontsize=14) plt.ylabel("Energy (Ha)", fontsize=14) plt.grid() -plt.axhline(y=true_energy, color="black", linestyle="dashed", label="True energy") +plt.axhline(y=true_energy, color="black", linestyle="--", label="True energy") plt.legend(fontsize=14) @@ -448,23 +443,19 @@ def circuit(param): # ^^^^^^^^^^^^^ # # Now let's perform the same experiment using SPSA for the VQE optimization. -# SPSA should use only 2 device executions per term in the expectation value. +# SPSA should use only 2 circuit executions per term in the expectation value. # Since there are 15 terms and we choose 160 iterations with two evaluations for # each gradient estimate, we expect 4800 total device -# executions. Again we create a new device and cost function in order to reset -# the number of executions. - -noisy_device = qml.device( - "qiskit.aer", wires=num_qubits, shots=1000, noise_model=noise_model -) -cost_function = qml.QNode(circuit, noisy_device) +# executions. num_steps_spsa = 160 opt = qml.SPSAOptimizer(maxiter=num_steps_spsa, c=0.3, a=1.5) -# Run the SPSA algorithm +# We spend 2 * 15 circuit evaluations per step, as there are 15 Hamiltonian terms +execs_per_step = 2 * 15 +# Run the optimization cost_history_spsa, exec_history_spsa = run_optimizer( - opt, cost_function, init_param, num_steps_spsa, 20 + opt, cost_function, init_param, num_steps_spsa, 20, execs_per_step ) final_energy = cost_history_spsa[-1] @@ -476,15 +467,16 @@ def circuit(param): ############################################################################## # The SPSA optimization seems to have found a similar energy value. # We again take a look at how the optimization curves compare, in particular -# with respect to the device executions spent on the task. +# with respect to the circuit executions spent on the task. plt.figure(figsize=(10, 6)) plt.plot(exec_history_grad, cost_history_grad, label="Gradient descent") plt.plot(exec_history_spsa, cost_history_spsa, label="SPSA") +plt.axhline(y=true_energy, color="black", linestyle="--", label="True energy") plt.title("$H_2$ energy from VQE using gradient descent vs. SPSA", fontsize=16) -plt.xlabel("Device executions", fontsize=14) +plt.xlabel("Circuit executions", fontsize=14) plt.ylabel("Energy (Ha)", fontsize=14) plt.grid() @@ -504,7 +496,7 @@ def circuit(param): # ---------- # # SPSA is a useful optimization technique that may be particularly beneficial on -# near-term quantum hardware. It uses significantly fewer device executions to achieve +# near-term quantum hardware. It uses significantly fewer circuit executions to achieve # comparable results as gradient-based methods, giving it the potential # to save time and resources. It can be a good alternative to # gradient-based methods when the optimization problem involves executing From ce4d5bb6b8ab62096face57f7cbef513b22b6d4b Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Fri, 14 Apr 2023 09:32:07 -0400 Subject: [PATCH 09/24] Updated adaptive circuits demo for new return type (#735) --- demonstrations/tutorial_adaptive_circuits.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_adaptive_circuits.py b/demonstrations/tutorial_adaptive_circuits.py index 0aa824d66b..fc9e9bdac3 100644 --- a/demonstrations/tutorial_adaptive_circuits.py +++ b/demonstrations/tutorial_adaptive_circuits.py @@ -346,7 +346,7 @@ def circuit(params): t1 = time.time() params, energy = opt.step_and_cost(circuit, params) t2 = time.time() - print("n = {:}, E = {:.8f} H, t = {:.2f} s".format(n, energy, t2 - t1)) + print("n = {:}, E = {:.8f} H, t = {:.2f} s".format(n, energy[0], t2 - t1)) ############################################################################## # Using the sparse method reproduces the ground state energy while the optimization time is From 7e4dfe98ade596ac476aa49f9dbb2509d1d51c4e Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Mon, 24 Apr 2023 14:56:04 -0400 Subject: [PATCH 10/24] Update automatically run demos to be able to execute with v0.30 (#741) * fix toric code and classical kernels demos (#740) * FIx photonics and noisy demos (#742) * FIx two demos * small change * wrap relevant results in np.array (#743) * Updated demos to work with return types (#744) * Update demos for new return types (#754) * remove pennylane-sf from dependencies * try to use developement lightning * force upgrade of lightnign * make tutorial_gbs non-executable --------- Co-authored-by: Romain Moyard Co-authored-by: Matthew Silverman Co-authored-by: Mudit Pandey Co-authored-by: lillian542 <38584660+lillian542@users.noreply.github.com> --- .github/workflows/build-branch.yml | 1 + .github/workflows/demo_diff_check.yml | 2 + ...al_gbs.metadata.json => gbs.metadata.json} | 0 demonstrations/{tutorial_gbs.py => gbs.py} | 0 ...torial_gbs_circuit.png => gbs_circuit.png} | Bin ...torial_gbs_circuit.svg => gbs_circuit.svg} | 0 ...rial_gbs_circuit2.png => gbs_circuit2.png} | Bin .../{tutorial_gbs_expt1.png => gbs_expt1.png} | Bin .../{tutorial_gbs_expt2.png => gbs_expt2.png} | Bin ...al_gbs_thumbnail.png => gbs_thumbnail.png} | Bin ...data.json => plugins_hybrid.metadata.json} | 2 +- ...al_plugins_hybrid.py => plugins_hybrid.py} | 0 demonstrations/tutorial_classical_kernels.py | 5 ++- demonstrations/tutorial_error_mitigation.py | 2 +- ...rial_gaussian_transformation.metadata.json | 2 +- .../tutorial_gaussian_transformation.py | 2 +- demonstrations/tutorial_geometric_qml.py | 11 +++--- .../tutorial_learning_from_experiments.py | 7 ++-- .../tutorial_local_cost_functions.py | 2 +- demonstrations/tutorial_noisy_circuits.py | 2 +- .../tutorial_photonics.metadata.json | 2 +- demonstrations/tutorial_photonics.py | 35 +++++++++++------- .../tutorial_quantum_transfer_learning.py | 2 +- .../tutorial_qubit_rotation.metadata.json | 2 +- demonstrations/tutorial_qubit_rotation.py | 2 +- demonstrations/tutorial_toric_code.py | 2 +- demos_getting-started.rst | 4 +- demos_quantum-computing.rst | 6 +-- requirements.txt | 1 - requirements_no_deps.txt | 3 +- what-is-quantum-computing.rst | 2 +- 31 files changed, 55 insertions(+), 44 deletions(-) rename demonstrations/{tutorial_gbs.metadata.json => gbs.metadata.json} (100%) rename demonstrations/{tutorial_gbs.py => gbs.py} (100%) rename demonstrations/{tutorial_gbs_circuit.png => gbs_circuit.png} (100%) rename demonstrations/{tutorial_gbs_circuit.svg => gbs_circuit.svg} (100%) rename demonstrations/{tutorial_gbs_circuit2.png => gbs_circuit2.png} (100%) rename demonstrations/{tutorial_gbs_expt1.png => gbs_expt1.png} (100%) rename demonstrations/{tutorial_gbs_expt2.png => gbs_expt2.png} (100%) rename demonstrations/{tutorial_gbs_thumbnail.png => gbs_thumbnail.png} (100%) rename demonstrations/{tutorial_plugins_hybrid.metadata.json => plugins_hybrid.metadata.json} (92%) rename demonstrations/{tutorial_plugins_hybrid.py => plugins_hybrid.py} (100%) diff --git a/.github/workflows/build-branch.yml b/.github/workflows/build-branch.yml index 2f70d586fd..2bfaf5e71d 100644 --- a/.github/workflows/build-branch.yml +++ b/.github/workflows/build-branch.yml @@ -231,6 +231,7 @@ jobs: run: | ${{ steps.venv.outputs.location }}/bin/python3 -m pip install -r requirements.txt ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --no-deps -r requirements_no_deps.txt + ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps --upgrade ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --upgrade pip 'setuptools<65' cmake - name: Download Worker Load Data Artifact diff --git a/.github/workflows/demo_diff_check.yml b/.github/workflows/demo_diff_check.yml index e77c4389f8..e47e85e200 100644 --- a/.github/workflows/demo_diff_check.yml +++ b/.github/workflows/demo_diff_check.yml @@ -35,6 +35,7 @@ jobs: python -m pip install --upgrade pip wheel pip install -r requirements.txt pip install --no-deps -r requirements_no_deps.txt + pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps - name: Build tutorials run: | @@ -79,6 +80,7 @@ jobs: python -m pip install --upgrade pip wheel pip install -r requirements.txt pip install --no-deps -r requirements_no_deps.txt + pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps - name: Build tutorials run: | diff --git a/demonstrations/tutorial_gbs.metadata.json b/demonstrations/gbs.metadata.json similarity index 100% rename from demonstrations/tutorial_gbs.metadata.json rename to demonstrations/gbs.metadata.json diff --git a/demonstrations/tutorial_gbs.py b/demonstrations/gbs.py similarity index 100% rename from demonstrations/tutorial_gbs.py rename to demonstrations/gbs.py diff --git a/demonstrations/tutorial_gbs_circuit.png b/demonstrations/gbs_circuit.png similarity index 100% rename from demonstrations/tutorial_gbs_circuit.png rename to demonstrations/gbs_circuit.png diff --git a/demonstrations/tutorial_gbs_circuit.svg b/demonstrations/gbs_circuit.svg similarity index 100% rename from demonstrations/tutorial_gbs_circuit.svg rename to demonstrations/gbs_circuit.svg diff --git a/demonstrations/tutorial_gbs_circuit2.png b/demonstrations/gbs_circuit2.png similarity index 100% rename from demonstrations/tutorial_gbs_circuit2.png rename to demonstrations/gbs_circuit2.png diff --git a/demonstrations/tutorial_gbs_expt1.png b/demonstrations/gbs_expt1.png similarity index 100% rename from demonstrations/tutorial_gbs_expt1.png rename to demonstrations/gbs_expt1.png diff --git a/demonstrations/tutorial_gbs_expt2.png b/demonstrations/gbs_expt2.png similarity index 100% rename from demonstrations/tutorial_gbs_expt2.png rename to demonstrations/gbs_expt2.png diff --git a/demonstrations/tutorial_gbs_thumbnail.png b/demonstrations/gbs_thumbnail.png similarity index 100% rename from demonstrations/tutorial_gbs_thumbnail.png rename to demonstrations/gbs_thumbnail.png diff --git a/demonstrations/tutorial_plugins_hybrid.metadata.json b/demonstrations/plugins_hybrid.metadata.json similarity index 92% rename from demonstrations/tutorial_plugins_hybrid.metadata.json rename to demonstrations/plugins_hybrid.metadata.json index ff2f2bd6c0..89d1bb51e2 100644 --- a/demonstrations/tutorial_plugins_hybrid.metadata.json +++ b/demonstrations/plugins_hybrid.metadata.json @@ -19,7 +19,7 @@ ], "seoDescription": "This tutorial introduces the notion of hybrid computation by combining several PennyLane device backends to train an algorithm containing both photonic and qubit devices.", "doi": "", - "canonicalURL": "https://pennylane.ai/qml/demos/tutorial_plugins_hybrid.html", + "canonicalURL": "https://pennylane.ai/qml/demos/plugins_hybrid.html", "references": [], "basedOnPapers": [], "referencedByPapers": [], diff --git a/demonstrations/tutorial_plugins_hybrid.py b/demonstrations/plugins_hybrid.py similarity index 100% rename from demonstrations/tutorial_plugins_hybrid.py rename to demonstrations/plugins_hybrid.py diff --git a/demonstrations/tutorial_classical_kernels.py b/demonstrations/tutorial_classical_kernels.py index cecc229c68..e57d743979 100644 --- a/demonstrations/tutorial_classical_kernels.py +++ b/demonstrations/tutorial_classical_kernels.py @@ -701,7 +701,10 @@ def probabilities_threshold_normalize(probabilities, thresh = 1.e-10): # itself, in case it is of use later: def fourier_q(d, thetas, amplitudes): - return np.real(coefficients(lambda x: QK(x, thetas, amplitudes), 1, d-1)) + def QK_partial(x): + squeezed_x = qml.math.squeeze(x) + return QK(squeezed_x, thetas, amplitudes) + return np.real(coefficients(QK_partial, 1, d-1)) ############################################################################### # And with this, we can finally visualize how the Fourier spectrum of the diff --git a/demonstrations/tutorial_error_mitigation.py b/demonstrations/tutorial_error_mitigation.py index 84cd251105..66f5238894 100644 --- a/demonstrations/tutorial_error_mitigation.py +++ b/demonstrations/tutorial_error_mitigation.py @@ -316,7 +316,7 @@ def executor(circuits, dev=dev_noisy): # Run extrapolation zero_noise = fac.reduce() -print(f"ZNE result: {zero_noise[0]}") +print(f"ZNE result: {zero_noise}") ############################################################################## # Let's make a plot of the data and fitted extrapolation function. diff --git a/demonstrations/tutorial_gaussian_transformation.metadata.json b/demonstrations/tutorial_gaussian_transformation.metadata.json index 2b3f4ce831..1980ecf173 100644 --- a/demonstrations/tutorial_gaussian_transformation.metadata.json +++ b/demonstrations/tutorial_gaussian_transformation.metadata.json @@ -26,7 +26,7 @@ "relatedContent": [ { "type": "demonstration", - "id": "tutorial_plugins_hybrid", + "id": "plugins_hybrid", "weight": 1.0 }, { diff --git a/demonstrations/tutorial_gaussian_transformation.py b/demonstrations/tutorial_gaussian_transformation.py index 210ab6f5da..de9d63fea8 100644 --- a/demonstrations/tutorial_gaussian_transformation.py +++ b/demonstrations/tutorial_gaussian_transformation.py @@ -9,7 +9,7 @@ :property="og:image": https://pennylane.ai/qml/_images/gauss-circuit.png .. related:: - tutorial_plugins_hybrid Plugins and Hybrid computation + plugins_hybrid Plugins and Hybrid computation quantum_neural_net Function fitting with a photonic quantum neural network qonn Optimizing a quantum optical neural network diff --git a/demonstrations/tutorial_geometric_qml.py b/demonstrations/tutorial_geometric_qml.py index 1a70803e51..adacad158b 100644 --- a/demonstrations/tutorial_geometric_qml.py +++ b/demonstrations/tutorial_geometric_qml.py @@ -678,7 +678,7 @@ def encode_game(game): # calculate the mean square error for this classification problem def cost_function(params, input, target): - output = torch.stack([circuit(x, params) for x in input]) + output = torch.stack([torch.hstack(circuit(x, params)) for x in input]) vec = output - target sum_sqr = torch.sum(vec * vec, dim=1) return torch.mean(sum_sqr) @@ -707,7 +707,7 @@ def cost_function(params, input, target): def accuracy(p, x_val, y_val): with torch.no_grad(): y_val = torch.tensor(y_val) - y_out = torch.stack([circuit(x, p) for x in x_val]) + y_out = torch.stack([torch.hstack(circuit(x, p)) for x in x_val]) acc = torch.sum(torch.argmax(y_out, axis=1) == torch.argmax(y_val, axis=1)) return acc / len(x_val) @@ -764,7 +764,7 @@ def opt_func(): def cost_function_no_sym(params, input, target): - output = torch.stack([circuit_no_sym(x, params) for x in input]) + output = torch.stack([torch.hstack(circuit_no_sym(x, params)) for x in input]) vec = output - target sum_sqr = torch.sum(vec * vec, dim=1) return torch.mean(sum_sqr) @@ -781,7 +781,7 @@ def cost_function_no_sym(params, input, target): def accuracy_no_sym(p, x_val, y_val): with torch.no_grad(): y_val = torch.tensor(y_val) - y_out = torch.stack([circuit_no_sym(x, p) for x in x_val]) + y_out = torch.stack([torch.hstack(circuit_no_sym(x, p)) for x in x_val]) acc = torch.sum(torch.argmax(y_out, axis=1) == torch.argmax(y_val, axis=1)) return acc / len(x_val) @@ -832,7 +832,6 @@ def opt_func(): from matplotlib import pyplot as plt -plt.style.use("seaborn") plt.title("Validation accuracies") plt.plot(saved_accs_sym, "b", label="Symmetric") plt.plot(saved_accs, "g", label="Standard") @@ -852,7 +851,7 @@ def opt_func(): # ###################################################################### -# The use of symmetries in both quantum and classsical machine learning is a developing field, so we +# The use of symmetries in both quantum and classical machine learning is a developing field, so we # can expect new results to emerge over the coming years. If you want to get # involved, the references given below are a great place to start. diff --git a/demonstrations/tutorial_learning_from_experiments.py b/demonstrations/tutorial_learning_from_experiments.py index b992993a6f..28102d3518 100644 --- a/demonstrations/tutorial_learning_from_experiments.py +++ b/demonstrations/tutorial_learning_from_experiments.py @@ -199,9 +199,9 @@ def circuit(ts=False): # the measurement outcomes for the first 3 shots circuit = generate_circuit(n_shots) -print(circuit(ts=True)[:, 0:3]) +print(np.array(circuit(ts=True))[:, 0:3]) print("\n") -print(circuit(ts=False)[:, 0:3]) +print(np.array(circuit(ts=False))[:, 0:3]) ###################################################################### @@ -231,6 +231,7 @@ def circuit(ts=False): def process_data(raw_data): "convert raw data to vectors of means and variances of each qubit" + raw_data = np.array(raw_data) nc = len(raw_data) # the number of circuits used to generate the data nq = len(raw_data[0]) # the number of qubits in each circuit new_data = np.zeros([nc, 2 * nq]) @@ -437,7 +438,7 @@ def enhanced_circuit(ts=False): # If we look at the raw measurement data for the T-symmetric unitaries: # -raw_data[0][:, 0:5] # outcomes of first 5 shots of the first T-symmetric circuit +np.array(raw_data[0])[:, 0:5] # outcomes of first 5 shots of the first T-symmetric circuit ###################################################################### diff --git a/demonstrations/tutorial_local_cost_functions.py b/demonstrations/tutorial_local_cost_functions.py index 580d64a851..9d0bd016b9 100644 --- a/demonstrations/tutorial_local_cost_functions.py +++ b/demonstrations/tutorial_local_cost_functions.py @@ -129,7 +129,7 @@ def local_cost_simple(rotations): local_circuit = qml.QNode(local_cost_simple, dev, interface="autograd") def cost_local(rotations): - return 1 - np.sum(local_circuit(rotations)[:,0])/wires + return 1 - np.sum([i for (i, _) in local_circuit(rotations)])/wires def cost_global(rotations): diff --git a/demonstrations/tutorial_noisy_circuits.py b/demonstrations/tutorial_noisy_circuits.py index 5bc14bfd35..9736f50120 100644 --- a/demonstrations/tutorial_noisy_circuits.py +++ b/demonstrations/tutorial_noisy_circuits.py @@ -231,7 +231,7 @@ def depolarizing_circuit(p): # ensure that the trainable parameters give rise to a valid channel parameter, i.e., a number # between 0 and 1. # -ev = np.tensor(0.7781, requires_grad=False) # observed expectation value +ev = 0.7781 # observed expectation value def sigmoid(x): return 1/(1+np.exp(-x)) diff --git a/demonstrations/tutorial_photonics.metadata.json b/demonstrations/tutorial_photonics.metadata.json index 11da233580..bca7cd5ad3 100644 --- a/demonstrations/tutorial_photonics.metadata.json +++ b/demonstrations/tutorial_photonics.metadata.json @@ -134,7 +134,7 @@ }, { "type": "demonstration", - "id": "tutorial_gbs", + "id": "gbs", "weight": 1.0 } ] diff --git a/demonstrations/tutorial_photonics.py b/demonstrations/tutorial_photonics.py index f042bc0527..61fc2429a8 100644 --- a/demonstrations/tutorial_photonics.py +++ b/demonstrations/tutorial_photonics.py @@ -11,7 +11,7 @@ tutorial_pasqal Quantum computation with neutral atoms tutorial_trapped_ions Trapped ion quantum computing tutorial_sc_qubits Quantum computing with superconducting qubits - tutorial_gbs Quantum advantage with Gaussian Boson Sampling + gbs Quantum advantage with Gaussian Boson Sampling *Author: Alvaro Ballon — Posted: 31 May 2022. Last updated: 16 June 2022.* @@ -168,8 +168,8 @@ def vacuum_measure_p(): # Sample measurements in phase space -x_sample = vacuum_measure_x().numpy() -p_sample = vacuum_measure_p().numpy() +x_sample = vacuum_measure_x() +p_sample = vacuum_measure_p() # Import some libraries for a nicer plot from scipy.stats import gaussian_kde @@ -287,8 +287,8 @@ def measure_coherent_p(alpha, phi): # Choose alpha and phi and sample 1000 measurements -x_sample_coherent = measure_coherent_x(3, np.pi / 3).numpy() -p_sample_coherent = measure_coherent_p(3, np.pi / 3).numpy() +x_sample_coherent = measure_coherent_x(3, np.pi / 3) +p_sample_coherent = measure_coherent_p(3, np.pi / 3) # Plot as before xp = vstack([x_sample_coherent, p_sample_coherent]) @@ -514,8 +514,8 @@ def measure_squeezed_p(r): # Choose alpha and phi and sample 1000 measurements -x_sample_squeezed = measure_squeezed_x(0.4).numpy() -p_sample_squeezed = measure_squeezed_p(0.4).numpy() +x_sample_squeezed = measure_squeezed_x(0.4) +p_sample_squeezed = measure_squeezed_p(0.4) # Plot as before xp = vstack([x_sample_squeezed, p_sample_squeezed]) @@ -617,22 +617,29 @@ def measurement(a, phi): @qml.qnode(dev_exact2) -def measurement2(a, theta, alpha, phi): +def measurement2_0(a, theta, alpha, phi): qml.Displacement(a, theta, wires = 0) # We choose the initial to be a displaced vacuum qml.CoherentState(alpha, phi, wires = 1) # Prepare coherent as second qumode qml.Beamsplitter(np.pi / 4, 0, wires=[0, 1]) # Interfere both states - return qml.expval(qml.NumberOperator(0)), qml.expval(qml.NumberOperator(1)) # Read out N + return qml.expval(qml.NumberOperator(0)) # Read out N + +@qml.qnode(dev_exact2) +def measurement2_1(a, theta, alpha, phi): + qml.Displacement(a, theta, wires = 0) # We choose the initial to be a displaced vacuum + qml.CoherentState(alpha, phi, wires = 1) # Prepare coherent as second qumode + qml.Beamsplitter(np.pi / 4, 0, wires=[0, 1]) # Interfere both states + return qml.expval(qml.NumberOperator(1)) # Read out N print( - "Expectation value of x-quadrature after displacement: {}\n".format(measurement(3, 0).numpy()) + "Expectation value of x-quadrature after displacement: {}\n".format(measurement(3, 0)) ) print("Expected current in each detector:") -print("Detector 1: {}".format(measurement2(3, 0, 1, 0)[0].numpy())) -print("Detector 2: {}".format(measurement2(3, 0, 1, 0)[1].numpy())) +print("Detector 1: {}".format(measurement2_0(3, 0, 1, 0))) +print("Detector 2: {}".format(measurement2_1(3, 0, 1, 0))) print( "Difference between currents: {}".format( - measurement2(3, 0, 1, 0)[1].numpy() - measurement2(3, 0, 1, 0)[0].numpy() + measurement2_1(3, 0, 1, 0) - measurement2_0(3, 0, 1, 0) ) ) @@ -683,7 +690,7 @@ def measurement2(a, theta, alpha, phi): # A Gaussian Boson Sampling circuit. The beamsplitters here may include phase shifts. # # Gaussian boson sampling (GBS) is interesting on its own -# (see :doc:`this tutorial ` for an in-depth discussion). +# (see :doc:`this tutorial ` for an in-depth discussion). # So far, two quantum devices have used large-scale versions of this circuit # to achieve quantum advantage on a particular computation, which involves sampling from # a probability distribution that classical computers take too long to simulate. In 2019, USTC's Jiuzhang device took 200 seconds diff --git a/demonstrations/tutorial_quantum_transfer_learning.py b/demonstrations/tutorial_quantum_transfer_learning.py index 94f6a4cf19..b495668f05 100644 --- a/demonstrations/tutorial_quantum_transfer_learning.py +++ b/demonstrations/tutorial_quantum_transfer_learning.py @@ -381,7 +381,7 @@ def forward(self, input_features): q_out = torch.Tensor(0, n_qubits) q_out = q_out.to(device) for elem in q_in: - q_out_elem = quantum_net(elem, self.q_params).float().unsqueeze(0) + q_out_elem = torch.Tensor(quantum_net(elem, self.q_params)).float().unsqueeze(0) q_out = torch.cat((q_out, q_out_elem)) # return the two-dimensional prediction from the postprocessing layer diff --git a/demonstrations/tutorial_qubit_rotation.metadata.json b/demonstrations/tutorial_qubit_rotation.metadata.json index ec61aa28b8..a56a565eca 100644 --- a/demonstrations/tutorial_qubit_rotation.metadata.json +++ b/demonstrations/tutorial_qubit_rotation.metadata.json @@ -26,7 +26,7 @@ "relatedContent": [ { "type": "demonstration", - "id": "tutorial_plugins_hybrid", + "id": "plugins_hybrid", "weight": 1.0 }, { diff --git a/demonstrations/tutorial_qubit_rotation.py b/demonstrations/tutorial_qubit_rotation.py index acc9ab0b19..9ce3bfdf5d 100644 --- a/demonstrations/tutorial_qubit_rotation.py +++ b/demonstrations/tutorial_qubit_rotation.py @@ -11,7 +11,7 @@ .. related:: - tutorial_plugins_hybrid Plugins and hybrid computation + plugins_hybrid Plugins and hybrid computation tutorial_gaussian_transformation Gaussian transformation tutorial_state_preparation Training a quantum circuit with PyTorch diff --git a/demonstrations/tutorial_toric_code.py b/demonstrations/tutorial_toric_code.py index f0aa1bd316..07b33dcb86 100644 --- a/demonstrations/tutorial_toric_code.py +++ b/demonstrations/tutorial_toric_code.py @@ -448,7 +448,7 @@ def excitations(x_sites, z_sites): # expectation value is :math:`-1`, then a quasiparticle exists in that location. # -occupation_numbers = lambda expvals: 0.5 * (1 - expvals) +occupation_numbers = lambda expvals: [0.5 * (1 - val) for val in expvals] def print_info(x_expvals, z_expvals): E = -sum(x_expvals) - sum(z_expvals) diff --git a/demos_getting-started.rst b/demos_getting-started.rst index 42855248ba..a6113d2c91 100644 --- a/demos_getting-started.rst +++ b/demos_getting-started.rst @@ -37,7 +37,7 @@ Here you can discover the basic tools needed to use PennyLane through simple dem .. gallery-item:: :tooltip: Use quantum machine learning in a multi-device quantum algorithm. :figure: demonstrations/plugins_hybrid/photon_redirection.png - :description: :doc:`demos/tutorial_plugins_hybrid` + :description: :doc:`demos/plugins_hybrid` :tags: autograd photonics strawberryfields .. gallery-item:: @@ -110,7 +110,7 @@ Here you can discover the basic tools needed to use PennyLane through simple dem demos/tutorial_qubit_rotation demos/tutorial_backprop demos/tutorial_adjoint_diff - demos/tutorial_plugins_hybrid + demos/plugins_hybrid demos/tutorial_noisy_circuits demos/tutorial_gaussian_transformation demos/braket-parallel-gradients diff --git a/demos_quantum-computing.rst b/demos_quantum-computing.rst index 8952a8b5d8..c34f87b641 100644 --- a/demos_quantum-computing.rst +++ b/demos_quantum-computing.rst @@ -56,8 +56,8 @@ such as benchmarking and characterizing quantum processors. .. gallery-item:: :tooltip: Construct and simulate a Gaussian Boson Sampler. - :figure: demonstrations/tutorial_gbs_thumbnail.png - :description: :doc:`demos/tutorial_gbs` + :figure: demonstrations/gbs_thumbnail.png + :description: :doc:`demos/gbs` :tags: photonics strawberryfields .. gallery-item:: @@ -133,7 +133,7 @@ such as benchmarking and characterizing quantum processors. demos/tutorial_classical_shadows demos/tutorial_pasqal demos/qsim_beyond_classical - demos/tutorial_gbs + demos/gbs demos/tutorial_trapped_ions demos/tutorial_error_mitigation demos/tutorial_sc_qubits diff --git a/requirements.txt b/requirements.txt index 4101f48662..00effcf679 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,7 +17,6 @@ cirq-core==0.14.1 cirq-pasqal==0.14.1 openfermionpyscf==0.5 # required by 2 tutorials: tutorial_quantum_chemistry and tutorial_adaptive_circuits and tutorial_diffable_shadows git+https://github.com/PennyLaneAI/pennylane.git -git+https://github.com/PennyLaneAI/pennylane-sf.git git+https://github.com/PennyLaneAI/pennylane-cirq.git git+https://github.com/PennyLaneAI/pennylane-qiskit.git qulacs==0.1.10.1 diff --git a/requirements_no_deps.txt b/requirements_no_deps.txt index 2cbcb376ed..4bb3a0b40e 100644 --- a/requirements_no_deps.txt +++ b/requirements_no_deps.txt @@ -1,3 +1,2 @@ mitiq==0.13.0 -pyquil==2.21 -pennylane-lightning \ No newline at end of file +pyquil==2.21 \ No newline at end of file diff --git a/what-is-quantum-computing.rst b/what-is-quantum-computing.rst index 872fc696be..fc660fbb1e 100644 --- a/what-is-quantum-computing.rst +++ b/what-is-quantum-computing.rst @@ -71,7 +71,7 @@ The bigger picture: quantum advantage Whether practical algorithmic speedups are possible in the NISQ regime remains an open problem, but some instances have already been demonstrated of `quantum devices solving computational problems `_ that would take classical computers an unfeasible amount of time. **Quantum computational advantage is already a reality**, -with an :doc:`increasing number ` of commercial and research organizations announcing their :doc:`breakthroughs `—some even +with an :doc:`increasing number ` of commercial and research organizations announcing their :doc:`breakthroughs `—some even making their devices publicly available for further research. Quantum computational advantage does not necessarily need to be demonstrated on problems that are thought of as From be299d65093950c88eaefdb81be5d0328000ab98 Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Mon, 24 Apr 2023 17:31:18 -0400 Subject: [PATCH 11/24] remove nlopt from list of requirements (#755) --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 00effcf679..f0e2e23c75 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,6 @@ autograd==1.5 numpy~=1.21 numba==0.53.1 networkx==2.5.1 -nlopt==2.6.2 semantic_version==2.10 strawberryfields==0.22 sympy==1.6.2 From 11991f50b33ad9d63302603f111a593a351fb97d Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Tue, 25 Apr 2023 10:04:08 -0400 Subject: [PATCH 12/24] use dev version of lightning for demo checker (#759) --- .github/workflows/demo_diff_check.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/demo_diff_check.yml b/.github/workflows/demo_diff_check.yml index e47e85e200..53f30c802d 100644 --- a/.github/workflows/demo_diff_check.yml +++ b/.github/workflows/demo_diff_check.yml @@ -35,7 +35,7 @@ jobs: python -m pip install --upgrade pip wheel pip install -r requirements.txt pip install --no-deps -r requirements_no_deps.txt - pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps + pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps --upgrade - name: Build tutorials run: | @@ -80,7 +80,7 @@ jobs: python -m pip install --upgrade pip wheel pip install -r requirements.txt pip install --no-deps -r requirements_no_deps.txt - pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps + pip install pennylane-lightning - name: Build tutorials run: | From d494b50486d198be21fb82e77382af935f6d2ca0 Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Wed, 26 Apr 2023 09:12:23 -0400 Subject: [PATCH 13/24] Improve formatting of output in toric code demo (#762) * round outputs on toric code * Update demonstrations/tutorial_toric_code.py Co-authored-by: Mudit Pandey --------- Co-authored-by: Matthew Silverman Co-authored-by: Mudit Pandey --- demonstrations/tutorial_toric_code.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/demonstrations/tutorial_toric_code.py b/demonstrations/tutorial_toric_code.py index 07b33dcb86..871edd7b39 100644 --- a/demonstrations/tutorial_toric_code.py +++ b/demonstrations/tutorial_toric_code.py @@ -333,8 +333,8 @@ def circuit(): E0 = -sum(xgroup_expvals) - sum(zgroup_expvals) -print("X Group expectation values", xgroup_expvals) -print("Z Group expectation values", zgroup_expvals) +print("X Group expectation values", [np.round(val) for val in xgroup_expvals]) +print("Z Group expectation values", [np.round(val) for val in zgroup_expvals]) print("Total energy: ", E0) @@ -436,8 +436,8 @@ def excitations(x_sites, z_sites): x_expvals, z_expvals = separate_expvals(excitations(single_x, [])) -print("XGroup: ", x_expvals) -print("ZGroup: ", z_expvals) +print("XGroup: ", [np.round(val) for val in x_expvals]) +print("ZGroup: ", [np.round(val) for val in z_expvals]) ###################################################################### @@ -448,7 +448,7 @@ def excitations(x_sites, z_sites): # expectation value is :math:`-1`, then a quasiparticle exists in that location. # -occupation_numbers = lambda expvals: [0.5 * (1 - val) for val in expvals] +occupation_numbers = lambda expvals: [0.5 * (1 - np.round(val)) for val in expvals] def print_info(x_expvals, z_expvals): E = -sum(x_expvals) - sum(z_expvals) From 30a6039807ea8da6da5c8aacbb8c90f38ca246b2 Mon Sep 17 00:00:00 2001 From: Matthew Silverman Date: Thu, 27 Apr 2023 11:50:11 -0400 Subject: [PATCH 14/24] use lightning in quantumvolume; fix qnspsa for new return; use H.sparse_matrix (#757) * use lightning in quantumvolume; fix qnspsa for new return * stop using sparse_hamiltonian --- demonstrations/qnspsa.py | 10 +++++----- demonstrations/quantum_volume.py | 2 +- demonstrations/tutorial_ml_classical_shadows.py | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/demonstrations/qnspsa.py b/demonstrations/qnspsa.py index 4ed9c8d465..f859b5f48d 100644 --- a/demonstrations/qnspsa.py +++ b/demonstrations/qnspsa.py @@ -383,7 +383,7 @@ def get_overlap_tape(qnode, params1, params2): def get_state_overlap(tape): - return qml.execute([tape], dev, None)[0][0][0] + return qml.execute([tape], dev, None)[0][0] ###################################################################### @@ -810,10 +810,10 @@ def __post_process_tensor(self, tensor_raw_results, tensor_dirs): # is a tensor of shape (d x d), d being the dimension of the input parameter # to the ansatz. tensor_finite_diff = ( - tensor_raw_results[0][0][0] - - tensor_raw_results[1][0][0] - - tensor_raw_results[2][0][0] - + tensor_raw_results[3][0][0] + tensor_raw_results[0][0] + - tensor_raw_results[1][0] + - tensor_raw_results[2][0] + + tensor_raw_results[3][0] ) metric_tensor = ( -( diff --git a/demonstrations/quantum_volume.py b/demonstrations/quantum_volume.py index 86af1d07d0..4c052bb141 100644 --- a/demonstrations/quantum_volume.py +++ b/demonstrations/quantum_volume.py @@ -378,7 +378,7 @@ def qv_circuit_layer(num_qubits): # need to run the same random circuit on two devices independently. num_qubits = 5 -dev_ideal = qml.device("default.qubit", shots=None, wires=num_qubits) +dev_ideal = qml.device("lightning.qubit", shots=None, wires=num_qubits) m = 3 # number of qubits diff --git a/demonstrations/tutorial_ml_classical_shadows.py b/demonstrations/tutorial_ml_classical_shadows.py index 3d2d4e12e0..761b1a063f 100644 --- a/demonstrations/tutorial_ml_classical_shadows.py +++ b/demonstrations/tutorial_ml_classical_shadows.py @@ -168,7 +168,7 @@ def corr_function(i, j): import scipy as sp ham = Hamiltonian(J_mat) -eigvals, eigvecs = sp.sparse.linalg.eigs(qml.utils.sparse_hamiltonian(ham)) +eigvals, eigvecs = sp.sparse.linalg.eigs(ham.sparse_matrix()) psi0 = eigvecs[:, np.argmin(eigvals)] @@ -516,7 +516,7 @@ def build_dataset(num_points, Nr, Nc, T=500): for coupling_mat in coupling_mats: ham = Hamiltonian(coupling_mat) - eigvals, eigvecs = sp.sparse.linalg.eigs(qml.utils.sparse_hamiltonian(ham)) + eigvals, eigvecs = sp.sparse.linalg.eigs(ham.sparse_matrix()) psi = eigvecs[:, np.argmin(eigvals)] shadow = gen_class_shadow(circuit_oshot, psi, T, num_qubits) From 1ce2d79495ccda71299b868a668124116f372c14 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Fri, 28 Apr 2023 15:25:25 -0400 Subject: [PATCH 15/24] Update manually run demos (#766) * Added changes to manual demos * Updated output as found in rerun * Trigger CI --- demonstrations/adjoint_diff/scaling.png | Bin 60503 -> 61113 bytes demonstrations/ensemble_multi_qpu.py | 23 +++++++++++------- .../ensemble_multi_qpu_001.png | Bin 42002 -> 42002 bytes 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torch.Tensor(quantum_net(elem, self.q_params)).float().unsqueeze(0) + q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0) q_out = torch.cat((q_out, q_out_elem)) # return the two-dimensional prediction from the postprocessing layer diff --git a/demonstrations/vqe_parallel.npz b/demonstrations/vqe_parallel.npz index 774c342051e655eb8044fd3bfc9475b294e9ec29..1b1272dc3fe8b630ed4be198f2db4c971bab74db 100644 GIT binary patch delta 359 zcmeC<=;IIy@MdNaVSoTdhLt~lvQ8A*kW?$J&#*}A@&1x4r`)gK{IH+p#-)-k8lU!; z<@J8ddHiv|NRVgII_;19y=s_?L|Z=We_+1uF7yBQ`?pN%owG^&{r=cuJI}3)-t9ld zZW@@Y`ffj}E$rTh5+)$RSgx=*`1*(aTeh2pv^syh96y>7{~ANC))(<&n+_i4XD zYPm`EVJGMu>H9I+|`biLERtr-%ZO~^N8oeewq3^f&bsU+b=CIRlK#~-Tuq! zIlI59z1xp!%bVl-UQJeE+>jLPwrVPO$m9K|lQ++oy8mJSu5AUiQ6``Ee_G>a@c!7x z{jA&d_14IL+<(#Xu+iD}5Bu3VWu>k?c)x$$`yj^)Jn#1>nay*Y(D-hDPh|he4i|K5 zn%1r7oNUIlB}MAupWnyaAMZD|ushrK|HFQ%Hvc=vv_I`XmHhQ!+t-i#^{Qpp1lWDt z?>4IYSHJM>_OHrGKnywhT3F5 XW-Au3$NZSZCNE-EVF6ozirEDKWnHOs From ad2e71db91050fb81b0ef8d6874cf0eca5ce112d Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Fri, 28 Apr 2023 17:16:01 -0400 Subject: [PATCH 16/24] Add warnings to pennylane-sf plugins and expected output (#761) * add warnings to pennylane-sf plugins, add output * Apply suggestions from code review Co-authored-by: Tom Bromley <49409390+trbromley@users.noreply.github.com> Co-authored-by: Josh Izaac * add warning to quantum neural net --------- Co-authored-by: Tom Bromley <49409390+trbromley@users.noreply.github.com> Co-authored-by: Josh Izaac --- demonstrations/gbs.py | 103 ++++++++++++++++++++++++++- demonstrations/plugins_hybrid.py | 94 ++++++++++++++++++++++++ demonstrations/qonn.py | 3 + demonstrations/quantum_neural_net.py | 4 +- 4 files changed, 201 insertions(+), 3 deletions(-) diff --git a/demonstrations/gbs.py b/demonstrations/gbs.py index f57a171385..4b1efa785a 100644 --- a/demonstrations/gbs.py +++ b/demonstrations/gbs.py @@ -19,6 +19,9 @@ *Authors: Josh Izaac and Nathan Killoran — Posted: 04 December 2020. Last updated: 04 December 2020.* +.. warning:: + This demo is only compatible with PennyLane version ``0.29`` or below. + On the journey to large-scale fault-tolerant quantum computers, one of the first major milestones is to demonstrate a quantum device carrying out tasks that are beyond the reach of any classical algorithm. The launch of Xanadu's Borealis device marked an important milestone @@ -147,7 +150,22 @@ U = unitary_group.rvs(4) print(U) -###################################################################### +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# [[ 0.23648826-0.48221431j 0.06829648+0.04447898j 0.51150074-0.09529866j +# 0.55205719-0.35974699j] +# [-0.11148167+0.69780321j -0.24943828+0.08410701j 0.46705929-0.43192981j +# 0.16220654-0.01817602j] +# [-0.22351926-0.25918352j 0.24364996-0.05375623j -0.09259829-0.53810588j +# 0.27267708+0.66941977j] +# [ 0.11519953-0.28596729j -0.90164923-0.22099186j -0.09627758-0.13105595j +# -0.0200152 +0.12766128j]] +# # We can now use this to construct the circuit, choosing a compatible # device. For the simulation, we can use the Strawberry Fields # Gaussian backend. This backend is perfectly suited for simulation of GBS, @@ -197,6 +215,15 @@ def gbs_circuit(): probs = gbs_circuit().reshape([cutoff] * n_wires) print(probs.shape) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# (10, 10, 10, 10) +# ###################################################################### # For example, element ``[1,2,0,1]`` represents the probability of # detecting 1 photon on wire @@ -214,6 +241,19 @@ def gbs_circuit(): for i in measure_states: print(f"|{''.join(str(j) for j in i)}>: {probs[i]}") +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# |0000>: 0.17637844761413496 +# |1100>: 0.03473293649420282 +# |0101>: 0.011870900427255589 +# |1111>: 0.005957399165336106 +# |2000>: 0.02957384308320549 +# ###################################################################### # The GBS Distribution # -------------------- @@ -292,6 +332,16 @@ def gbs_circuit(): print(A[:, [0, 1]][[0, 1]]) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# [[ 0.19343159-0.54582922j 0.43418269-0.09169615j] +# [ 0.43418269-0.09169615j -0.27554025-0.46222197j]] +# ###################################################################### # i.e., we consider only the rows and columns where a photon was detected, which gives us # the submatrix corresponding to indices :math:`0` and :math:`1`. @@ -310,6 +360,16 @@ def gbs_circuit(): print(1 / np.cosh(1) ** 4) print(probs[0, 0, 0, 0]) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.1763784476141347 +# 0.17637844761413496 +# ###################################################################### # **Measuring** :math:`|1,1,0,0\rangle` **at the output** @@ -317,6 +377,16 @@ def gbs_circuit(): print(np.abs(haf(A)) ** 2 / np.cosh(1) ** 4) print(probs[1, 1, 0, 0]) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.03473293649420271 +# 0.03473293649420282 +# ###################################################################### # **Measuring** :math:`|0,1,0,1\rangle` **at the output** @@ -324,6 +394,16 @@ def gbs_circuit(): print(np.abs(haf(A)) ** 2 / np.cosh(1) ** 4) print(probs[0, 1, 0, 1]) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.011870900427255558 +# 0.011870900427255589 +# ###################################################################### # **Measuring** :math:`|1,1,1,1\rangle` **at the output** # @@ -333,6 +413,16 @@ def gbs_circuit(): print(np.abs(haf(A)) ** 2 / np.cosh(1) ** 4) print(probs[1, 1, 1, 1]) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.005957399165336081 +# 0.005957399165336106 +# ###################################################################### # **Measuring** :math:`|2,0,0,0\rangle` **at the output** # @@ -343,7 +433,16 @@ def gbs_circuit(): print(np.abs(haf(A)) ** 2 / (2 * np.cosh(1) ** 4)) print(probs[2, 0, 0, 0]) -###################################################################### +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.029573843083205383 +# 0.02957384308320549 +# # The PennyLane simulation results agree (with almost negligible numerical error) to the # expected result from the Gaussian boson sampling equation! # diff --git a/demonstrations/plugins_hybrid.py b/demonstrations/plugins_hybrid.py index b3bd6669c7..fc7df39cbb 100644 --- a/demonstrations/plugins_hybrid.py +++ b/demonstrations/plugins_hybrid.py @@ -24,6 +24,9 @@ Be sure to read through the introductory :ref:`qubit rotation ` and :ref:`Gaussian transformation ` tutorials before attempting this tutorial. +.. warning:: + This demo is only compatible with PennyLane version ``0.29`` or below. + .. note:: To follow along with this tutorial on your own computer, you will require the @@ -195,6 +198,16 @@ def cost(params): init_params = np.array([0.01, 0.01], requires_grad=True) print(cost(init_params)) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# -9.999666671111081e-05 +# + ############################################################################## # Here, we choose the values of :math:`\theta` and :math:`\phi` to be very close to zero; # this results in :math:`B(\theta,\phi)\approx I`, and the output of the quantum @@ -212,6 +225,16 @@ def cost(params): dphoton_redirection = qml.grad(photon_redirection, argnum=0) print(dphoton_redirection([0.0, 0.0])) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# [array(0.), array(0.)] +# + ############################################################################## # Now, let's use the :class:`~.pennylane.GradientDescentOptimizer`, and update the circuit # parameters over 100 optimization steps. @@ -233,6 +256,36 @@ def cost(params): print("Optimized rotation angles: {}".format(params)) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Cost after step 5: -0.0349558 +# Cost after step 10: -0.9969017 +# Cost after step 15: -1.0000000 +# Cost after step 20: -1.0000000 +# Cost after step 25: -1.0000000 +# Cost after step 30: -1.0000000 +# Cost after step 35: -1.0000000 +# Cost after step 40: -1.0000000 +# Cost after step 45: -1.0000000 +# Cost after step 50: -1.0000000 +# Cost after step 55: -1.0000000 +# Cost after step 60: -1.0000000 +# Cost after step 65: -1.0000000 +# Cost after step 70: -1.0000000 +# Cost after step 75: -1.0000000 +# Cost after step 80: -1.0000000 +# Cost after step 85: -1.0000000 +# Cost after step 90: -1.0000000 +# Cost after step 95: -1.0000000 +# Cost after step 100: -1.0000000 +# Optimized rotation angles: [1.57079633 0.01 ] +# +# ############################################################################## # Comparing this to the :ref:`exact calculation ` above, @@ -342,6 +395,37 @@ def cost(params, phi1=0.5, phi2=0.1): print("Optimized rotation angles: {}".format(params)) + +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Cost after step 5: 0.2154539 +# Cost after step 10: 0.0000982 +# Cost after step 15: 0.0000011 +# Cost after step 20: 0.0000000 +# Cost after step 25: 0.0000000 +# Cost after step 30: 0.0000000 +# Cost after step 35: 0.0000000 +# Cost after step 40: 0.0000000 +# Cost after step 45: 0.0000000 +# Cost after step 50: 0.0000000 +# Cost after step 55: 0.0000000 +# Cost after step 60: 0.0000000 +# Cost after step 65: 0.0000000 +# Cost after step 70: 0.0000000 +# Cost after step 75: 0.0000000 +# Cost after step 80: 0.0000000 +# Cost after step 85: 0.0000000 +# Cost after step 90: 0.0000000 +# Cost after step 95: 0.0000000 +# Cost after step 100: 0.0000000 +# Optimized rotation angles: [1.20671364 0.01 ] +# +# ############################################################################## # Substituting this into the photon redirection QNode shows that it now produces # the same output as the qubit rotation QNode: @@ -350,6 +434,16 @@ def cost(params, phi1=0.5, phi2=0.1): print(photon_redirection(result)) print(qubit_rotation(0.5, 0.1)) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# 0.8731983021146449 +# 0.8731983044562817 +# ############################################################################## # This is just a simple example of the kind of hybrid computation that can be carried # out in PennyLane. Quantum nodes (bound to different devices) and classical diff --git a/demonstrations/qonn.py b/demonstrations/qonn.py index 7fb1b4e213..1466249d5e 100644 --- a/demonstrations/qonn.py +++ b/demonstrations/qonn.py @@ -14,6 +14,9 @@ *Author: Theodor Isacsson — Posted: 05 August 2020. Last updated: 08 March 2022.* +.. warning:: + This demo is only compatible with PennyLane version ``0.29`` or below. + This tutorial is based on a paper from `Steinbrecher et al. (2019) `__ which explores a Quantum Optical Neural Network (QONN) based on Fock states. Similar to the continuous-variable :doc:`quantum neural network diff --git a/demonstrations/quantum_neural_net.py b/demonstrations/quantum_neural_net.py index fa1cf13b76..bc88c3f26d 100644 --- a/demonstrations/quantum_neural_net.py +++ b/demonstrations/quantum_neural_net.py @@ -19,6 +19,9 @@ *Author: Maria Schuld — Posted: 11 October 2019. Last updated: 25 January 2021.* +.. warning:: + This demo is only compatible with PennyLane version ``0.29`` or below. + In this example we show how a variational circuit can be used to learn a fit for a one-dimensional function when being trained with noisy samples from that function. @@ -140,7 +143,6 @@ def cost(var, features, labels): plt.tick_params(axis="both", which="minor", labelsize=16) plt.show() - ############################################################################## # .. image:: ../demonstrations/quantum_neural_net/qnn_output_20_0.png # From 7b971bbebc2d963df5220e9e2cdb9677121b6e64 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Tue, 13 Jun 2023 15:44:20 -0400 Subject: [PATCH 17/24] dev (#824) --- requirements.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements.txt b/requirements.txt index 2fc27056dd..98331fa25a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,11 +15,11 @@ pyscf==2.1.1 cirq-core==0.14.1 cirq-pasqal==0.14.1 openfermionpyscf==0.5 # required by 2 tutorials: tutorial_quantum_chemistry and tutorial_adaptive_circuits and tutorial_diffable_shadows -pennylane==0.30.0 -pennylane-cirq==0.29.0 -pennylane-qiskit==0.30.1 +git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane +git+https://github.com/PennyLaneAI/pennylane-cirq.git#egg=pennylane-cirq +git+https://github.com/PennyLaneAI/pennylane-qiskit.git#egg=pennylane-qiskit qulacs==0.1.10.1 -pennylane-qulacs==0.29.0 +git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs scikit-learn==0.23.2 docutils==0.16 markupsafe==2.0.1 From bada2cf12a6322be135740b4d48b0e260566bb8b Mon Sep 17 00:00:00 2001 From: Tom Bromley <49409390+trbromley@users.noreply.github.com> Date: Fri, 16 Jun 2023 09:25:54 -0400 Subject: [PATCH 18/24] Update requirements.txt (#825) --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 98331fa25a..1990d07b6c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -39,8 +39,8 @@ matplotlib==3.5.3 noisyopt==0.2.2 Jinja2==2.11.3 pyyaml==5.4.1 -jax==0.4.1 -jaxlib==0.4.1 +jax==0.4.12 +jaxlib==0.4.12 neural-tangents==0.6.1 flamingpy>=0.10.1b1 plotly>=4.5.0 From 66cd2350cb97508d722a117c2bcef7a5a25ce9b7 Mon Sep 17 00:00:00 2001 From: soranjh <40344468+soranjh@users.noreply.github.com> Date: Fri, 16 Jun 2023 18:54:55 -0400 Subject: [PATCH 19/24] Update resource estimation demo for non-cubic systems (#823) --- ...tutorial_resource_estimation.metadata.json | 12 ++++---- .../tutorial_resource_estimation.py | 28 ++++++++++--------- 2 files changed, 21 insertions(+), 19 deletions(-) diff --git a/demonstrations/tutorial_resource_estimation.metadata.json b/demonstrations/tutorial_resource_estimation.metadata.json index 02b685c38d..ef4fdf21cc 100644 --- a/demonstrations/tutorial_resource_estimation.metadata.json +++ b/demonstrations/tutorial_resource_estimation.metadata.json @@ -40,13 +40,13 @@ "url": "https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.030305" }, { - "id": "su2021", - "title": "Fault-Tolerant Quantum Simulations of Chemistry in First Quantization", - "authors": "Yuan Su, Dominic W. Berry, Nathan Wiebe, Nicholas Rubin, and Ryan Babbush", - "year": "2021", - "journal": "PRX Quantum", + "id": "zini2023", + "title": "Quantum simulation of battery materials using ionic pseudopotentials", + "authors": "Modjtaba Shokrian Zini, Alain Delgado, Roberto dos Reis, et al.", + "year": "2023", + "journal": "arXiv:2302.07981", "doi": "", - "url": "https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.040332" + "url": "https://arxiv.org/abs/2302.07981" }, { "id": "delgado2022", diff --git a/demonstrations/tutorial_resource_estimation.py b/demonstrations/tutorial_resource_estimation.py index ab9b132534..9d1c176d2c 100644 --- a/demonstrations/tutorial_resource_estimation.py +++ b/demonstrations/tutorial_resource_estimation.py @@ -126,22 +126,23 @@ ############################################################################## # QPE cost for simulating periodic materials # ****************************************** -# For periodic materials, we estimate the cost of implementing the QPE algorithm of [#su2021]_ +# For periodic materials, we estimate the cost of implementing the QPE algorithm of [#zini2023]_ # using Hamiltonians represented in first quantization and in a plane wave basis. We first need to -# define the number of plane waves, the number of electrons, and the volume of the unit cell that -# constructs the periodic material. Let's use dilithium iron silicate +# define the number of plane waves, the number of electrons, and the lattice vectors that construct +# the unit cell of the periodic material. Let's use dilithium iron silicate # :math:`\text{Li}_2\text{FeSiO}_4` as an example taken from [#delgado2022]_. For this material, the # unit cell contains 156 electrons and has dimensions :math:`9.49 \times 10.20 \times 11.83` in -# atomic units, amounting to a volume of :math:`1145 a_0^3`, where :math:`a_0` is the -# `Bohr radius `_. We also use :math:`10^5` plane waves. +# `atomic units `_. We also use :math:`10^5` plane waves. planewaves = 100000 electrons = 156 -volume = 1145 +vectors = np.array([[9.49, 0.00, 0.00], + [0.00, 10.20, 0.00], + [0.00, 0.00, 11.83]]) ############################################################################## # We now create an instance of the :class:`~.pennylane.resource.FirstQuantization` class -algo = qml.resource.FirstQuantization(planewaves, electrons, volume) +algo = qml.resource.FirstQuantization(planewaves, electrons, vectors=vectors) ############################################################################## # and obtain the estimated number of non-Clifford gates and logical qubits. @@ -161,7 +162,7 @@ n_qubits_ = [] for pw in planewaves: - algo_ = qml.resource.FirstQuantization(pw, electrons, volume, error=er) + algo_ = qml.resource.FirstQuantization(pw, electrons, vectors=vectors, error=er) n_gates_.append(algo_.gates) n_qubits_.append(algo_.qubits) n_gates.append(n_gates_) @@ -296,12 +297,13 @@ # `PRX Quantum 2, 030305 (2021) # `__ # -# .. [#su2021] +# .. [#zini2023] # -# Yuan Su, Dominic W. Berry, Nathan Wiebe, Nicholas Rubin, and Ryan Babbush, -# "Fault-Tolerant Quantum Simulations of Chemistry in First Quantization". -# `PRX Quantum 2, 040332 (2021) -# `__ +# Modjtaba Shokrian Zini, Alain Delgado, Roberto dos Reis, Pablo A. M. Casares, +# Jonathan E. Mueller, Arne-Christian Voigt, Juan Miguel Arrazola, +# "Quantum simulation of battery materials using ionic pseudopotentials". +# ` arXiv:2302.07981 (2023) +# `__ # # .. [#delgado2022] # From b7b20da468d34fe0cae0ccb30127849dace55d3b Mon Sep 17 00:00:00 2001 From: Christina Lee Date: Tue, 20 Jun 2023 16:46:11 -0400 Subject: [PATCH 20/24] Change private methods in adjoint diff tutorial (#832) * adjoint diff tutorial improvement * Apply suggestions from code review Co-authored-by: Mudit Pandey Co-authored-by: Matthew Silverman * update date --------- Co-authored-by: Mudit Pandey Co-authored-by: Matthew Silverman --- demonstrations/tutorial_adjoint_diff.py | 66 +++++++++++++------------ 1 file changed, 35 insertions(+), 31 deletions(-) diff --git a/demonstrations/tutorial_adjoint_diff.py b/demonstrations/tutorial_adjoint_diff.py index 893c87e11f..73108c2b82 100644 --- a/demonstrations/tutorial_adjoint_diff.py +++ b/demonstrations/tutorial_adjoint_diff.py @@ -20,7 +20,7 @@ """ ############################################################################## -# *Author: Christina Lee. Posted: 23 Nov 2021. Last updated: 23 Nov 2021.* +# *Author: Christina Lee. Posted: 23 Nov 2021. Last updated: 20 Jun 2023.* # # `Classical automatic differentiation `__ # has two methods of calculation: forward and reverse. @@ -110,21 +110,26 @@ def circuit(a): M = qml.PauliX(wires=1) ############################################################################## -# We create our state by using the ``"default.qubit"`` methods ``_create_basis_state`` -# and ``_apply_operation``. +# We will be using internal functions to manipulate the nuts and bolts of a statevector +# simulation. +# +# Internally, the statevector simulation uses a 2x2x2x... array to represent the state, whereas +# the result of a measurement ``qml.state()`` flattens this internal representation. Each dimension +# in the statevector corresponds to a different qubit. +# +# The internal functions ``create_initial_state`` and ``apply_operation`` +# make additional assumptions about their inputs, and will fail or give incorrect results +# if those assumptions are not met. To work with these simulation tools, all operations should provide +# a matrix, and all wires must corresponding to dimensions of the statevector. This means all wires must already +# be integers starting from ``0``, and not exceed the number of dimensions in the state vector. # -# These are private methods that you shouldn't typically use and are subject to change -# without a deprecation period, -# but we use them here to illustrate the algorithm. -# -# Internally, the device uses a 2x2x2x... array to represent the state, whereas -# the measurement ``qml.state()`` and the device attribute ``dev.state`` -# flatten this internal representation. -state = dev._create_basis_state(0) +from pennylane.devices.qubit import create_initial_state, apply_operation + +state = create_initial_state((0, 1)) for op in ops: - state = dev._apply_operation(state, op) + state = apply_operation(op, state) print(state) @@ -145,7 +150,7 @@ def circuit(a): # Using the ``state`` calculated above, we can create these :math:`|b\rangle` and :math:`|k\rangle` # vectors. -bra = dev._apply_operation(state, M) +bra = apply_operation(M, state) ket = state ############################################################################## @@ -171,17 +176,17 @@ def circuit(a): # and gotten the exact same results. Here, the subscript :math:`n` is used to indicate that :math:`U_n` # was moved to the bra side of the expression. Let's calculate that instead: -bra_n = dev._create_basis_state(0) +bra_n = create_initial_state((0, 1)) for op in ops: - bra_n = dev._apply_operation(bra_n, op) -bra_n = dev._apply_operation(bra_n, M) -bra_n = dev._apply_operation(bra_n, qml.adjoint(ops[-1])) + bra_n = apply_operation(op, bra_n) +bra_n = apply_operation(M, bra_n) +bra_n = apply_operation(qml.adjoint(ops[-1]), bra_n) -ket_n = dev._create_basis_state(0) +ket_n = create_initial_state((0, 1)) for op in ops[:-1]: # don't apply last operation - ket_n = dev._apply_operation(ket_n, op) + ket_n = apply_operation(op, ket_n) M_expval_n = np.vdot(bra_n, ket_n) print(M_expval_n) @@ -210,13 +215,13 @@ def circuit(a): # # Let's call this the "version 2" method. -bra_n_v2 = dev._apply_operation(state, M) +bra_n_v2 = apply_operation(M, state) ket_n_v2 = state adj_op = qml.adjoint(ops[-1]) -bra_n_v2 = dev._apply_operation(bra_n_v2, adj_op) -ket_n_v2 = dev._apply_operation(ket_n_v2, adj_op) +bra_n_v2 = apply_operation(adj_op, bra_n_v2) +ket_n_v2 = apply_operation(adj_op, ket_n_v2) M_expval_n_v2 = np.vdot(bra_n_v2, ket_n_v2) print(M_expval_n_v2) @@ -240,13 +245,13 @@ def circuit(a): # For each iteration, we move an operation from the ket side to the bra side. # We start near the center at :math:`U_n` and reverse through the operations list until we reach :math:`U_0`. -bra_loop = dev._apply_operation(state, M) +bra_loop = apply_operation(M, state) ket_loop = state for op in reversed(ops): adj_op = qml.adjoint(op) - bra_loop = dev._apply_operation(bra_loop, adj_op) - ket_loop = dev._apply_operation(ket_loop, adj_op) + bra_loop = apply_operation(adj_op, bra_loop) + ket_loop = apply_operation(adj_op, ket_loop) print(np.vdot(bra_loop, ket_loop)) ############################################################################## @@ -345,27 +350,26 @@ def circuit(a): # We loop over the reversed operations, just as before. But if the operation has a parameter, # we calculate its derivative and append it to a list before moving on. Since the ``operation_derivative`` # function spits back out a matrix instead of an operation, -# we have to use ``dev._apply_unitary`` instead to create :math:`|\tilde{k}_i\rangle`. +# we have to use :class:`~pennylane.QubitUnitary` instead to create :math:`|\tilde{k}_i\rangle`. -bra = dev._apply_operation(state, M) +bra = apply_operation(M, state) ket = state grads = [] for op in reversed(ops): adj_op = qml.adjoint(op) - ket = dev._apply_operation(ket, adj_op) + ket = apply_operation(adj_op, ket) # Calculating the derivative if op.num_params != 0: dU = qml.operation.operation_derivative(op) - - ket_temp = dev._apply_unitary(ket, dU, op.wires) + ket_temp = apply_operation(qml.QubitUnitary(dU, op.wires), ket) dM = 2 * np.real(np.vdot(bra, ket_temp)) grads.append(dM) - bra = dev._apply_operation(bra, adj_op) + bra = apply_operation(adj_op, bra) # Finally reverse the order of the gradients From 21061d02c79c161e4b70ce287116b7909257bc7a Mon Sep 17 00:00:00 2001 From: soranjh <40344468+soranjh@users.noreply.github.com> Date: Fri, 23 Jun 2023 13:24:39 -0400 Subject: [PATCH 21/24] Add fermionic operators demo (#813) --- .../thumbnail_large_fermionic_operators.png | Bin 0 -> 236840 bytes .../fermionic_operators.jpg | Bin 0 -> 393175 bytes ...thumbnail_tutorial_fermionic_operators.png | Bin 0 -> 48674 bytes ...tutorial_fermionic_operators.metadata.json | 52 ++++ .../tutorial_fermionic_operators.py | 236 ++++++++++++++++++ demos_quantum-chemistry.rst | 7 + 6 files changed, 295 insertions(+) create mode 100644 _static/large_demo_thumbnails/thumbnail_large_fermionic_operators.png create mode 100644 demonstrations/fermionic_operators/fermionic_operators.jpg create mode 100644 demonstrations/fermionic_operators/thumbnail_tutorial_fermionic_operators.png create mode 100644 demonstrations/tutorial_fermionic_operators.metadata.json create mode 100644 demonstrations/tutorial_fermionic_operators.py diff --git 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b/demonstrations/tutorial_fermionic_operators.metadata.json @@ -0,0 +1,52 @@ +{ + "title": "Fermionic operators", + "authors": [ + { + "id": "soran_jahangiri" + } + ], + "dateOfPublication": "2023-06-27T00:00:00", + "dateOfLastModification": "2023-06-27T00:00:00", + "categories": [ + "Quantum Chemistry" + ], + "tags": [], + "previewImages": [ + { + "type": "thumbnail", + "uri": "/_images/thumbnail_fermionic_operators.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/large_demo_thumbnails/thumbnail_large_fermionic_operators.png" + } + ], + "seoDescription": "Construct Hamiltonians and other observables using fermionic creation and annihilation operators.", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_fermionic_operators", + "references": [ + { + "id": "surjan", + "title": "Second Quantized Approach to Quantum Chemistry", + "authors": "Peter R. Surjan", + "year": "1989", + "publisher": "Springer-Verlag", + "doi": "", + "url": "" + } + ], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_quantum_chemistry", + "weight": 1.0 + }, + { + "type": "demonstration", + "id": "tutorial_vqe", + "weight": 1.0 + } + ] +} \ No newline at end of file diff --git a/demonstrations/tutorial_fermionic_operators.py b/demonstrations/tutorial_fermionic_operators.py new file mode 100644 index 0000000000..b74bf4434b --- /dev/null +++ b/demonstrations/tutorial_fermionic_operators.py @@ -0,0 +1,236 @@ +r""" + +Fermionic operators +=================== + +.. meta:: + :property="og:description": Learn how to work with fermionic operators + :property="og:image": https://pennylane.ai/qml/_images/fermionic_operators.jpg + +.. related:: + tutorial_quantum_chemistry Building molecular Hamiltonians + tutorial_vqe A brief overview of VQE + +*Author: Soran Jahangiri — Posted: 27 June 2023. Last updated: 27 June 2023.* + +Fermionic creation and annihilation operators are commonly used to construct +`Hamiltonians `_ and other observables of molecules and spin +systems [#surjan]_. In this demo, you will learn how to use PennyLane to create fermionic operators +and map them to a qubit representation for use in quantum algorithms. + + +.. figure:: /demonstrations/fermionic_operators/fermionic_operators.jpg + :width: 60% + :align: center + + +Constructing fermionic operators +-------------------------------- +The fermionic `creation and annihilation `_ +operators can be constructed in PennyLane similarly to Pauli operators by using +:class:`~.pennylane.FermiC` and :class:`~.pennylane.FermiA` for creation and annihilation operators, +respectively. +""" + +from pennylane import FermiC, FermiA + +a0_dag = FermiC(0) +a1 = FermiA(1) + +############################################################################## +# We used the compact notations ``a0_dag`` to denote a creation operator applied to +# the :math:`0\text{th}` orbital and ``a1`` to denote an annihilation operator applied to the +# :math:`1\text{st}` orbital. Once created, these operators can be multiplied or added to each other +# to create new operators. A product of fermionic operators will be called a *Fermi word* and a +# linear combination of Fermi words will be called a *Fermi sentence*. + +fermi_word = a0_dag * a1 +fermi_sentence = 1.3 * a0_dag * a1 + 2.4 * a1 +fermi_sentence + +############################################################################## +# In this simple example, we first created the operator :math:`a^{\dagger}_0 a_1` and then created +# the linear combination :math:`1.3 a^{\dagger}_0 a_1 + 2.4 a_1`. We can also perform +# arithmetic operations between Fermi words and Fermi sentences. + +fermi_sentence = fermi_sentence * fermi_word + 2.3 * fermi_word +fermi_sentence + +############################################################################## +# Beyond multiplication, summation, and subtraction, we can exponentiate fermionic operators in +# PennyLane to an integer power. For instance, we can create a more complicated operator +# +# .. math:: +# +# 1.2 \times a_0^{\dagger} + 0.5 \times a_1 - 2.3 \times \left ( a_0^{\dagger} a_1 \right )^2, +# +# in the same way that you would write down the operator on a piece of paper: + +fermi_sentence = 1.2 * a0_dag + 0.5 * a1 - 2.3 * (a0_dag * a1) ** 2 +fermi_sentence + +############################################################################## +# This Fermi sentence can be mapped to the qubit basis using the +# `Jordan-Wigner `_ +# transformation to get a linear combination of Pauli operators. + +from pennylane import jordan_wigner + +pauli_sentence = jordan_wigner(fermi_sentence) +pauli_sentence + +############################################################################## +# Fermionic Hamiltonians +# ---------------------- +# Now that we have nice tools to create and manipulate fermionic operators, we can build some +# interesting fermionic Hamiltonians. +# +# A toy model +# ^^^^^^^^^^^ +# Our first example is a toy Hamiltonian inspired by the +# `Hückel method `_, which is a method for +# describing molecules with alternating single and double bonds. Our toy model is a simplified +# version of the Hückel Hamiltonian and assumes only two orbitals and a single electron. +# +# .. math:: +# +# H = \alpha \left (a^{\dagger}_0 a_0 + a^{\dagger}_1 a_1 \right ) + +# \beta \left (a^{\dagger}_0 a_1 + a^{\dagger}_1 a_0 \right ). +# +# This Hamiltonian can be constructed with pre-defined values :math:`\alpha = 0.01` and +# :math:`\beta = -0.02`. + +h1 = 0.01 * (FermiC(0) * FermiA(0) + FermiC(1) * FermiA(1)) +h2 = -0.02 * (FermiC(0) * FermiA(1) + FermiC(1) * FermiA(0)) +h = h1 + h2 +print(h) + +############################################################################## +# The fermionic Hamiltonian can be converted to the qubit Hamiltonian with: + +h = jordan_wigner(h) + +############################################################################## +# The matrix representation of the qubit Hamiltonian in the computational basis can be diagonalized +# to get its eigenpairs. + +from pennylane import numpy as np + +val, vec = np.linalg.eigh(h.sparse_matrix().toarray()) +print(f"eigenvalues:\n{val}") +print() +print(f"eigenvectors:\n{np.real(vec.T)}") + +############################################################################## +# +# Hydrogen molecule +# ^^^^^^^^^^^^^^^^^ +# The `second quantized `_ molecular electronic +# Hamiltonian is usually constructed as +# +# .. math:: +# H = \sum_{\alpha \in \{\uparrow, \downarrow \} } \sum_{pq} c_{pq} a_{p,\alpha}^{\dagger} +# a_{q, \alpha} + \frac{1}{2} \sum_{\alpha, \beta \in \{\uparrow, \downarrow \} } \sum_{pqrs} +# c_{pqrs} a_{p, \alpha}^{\dagger} a_{q, \beta}^{\dagger} a_{r, \beta} a_{s, \alpha}, +# +# where :math:`\alpha` and :math:`\beta` denote the electron spin and :math:`p, q, r, s` are the +# orbital indices. The coefficients :math:`c` are integrals over +# molecular orbitals that are obtained from +# `Hartree-Fock `_ +# calculations. These integrals can be computed with PennyLane using the +# :func:`~.pennylane.qchem.electron_integrals` function. We can build the molecular Hamiltonian for +# the hydrogen molecule as an example. We first define the atom types and the atomic coordinates. + +import pennylane as qml + +symbols = ["H", "H"] +geometry = np.array([[-0.67294, 0.0, 0.0], [0.67294, 0.0, 0.0]], requires_grad=False) + +############################################################################## +# Then we compute the one- and two-electron integrals, which are the coefficients :math:`c` in the +# second quantized molecular Hamiltonian defined above. We also obtain the core constant, which is +# later used to calculate the contribution of the nuclear energy to the Hamiltonian. + +mol = qml.qchem.Molecule(symbols, geometry) +core, one, two = qml.qchem.electron_integrals(mol)() + +############################################################################## +# These integrals are computed over molecular orbitals. Each molecular orbital contains a pair of +# electrons with different spins. We have assumed that the spatial distribution of these electron +# pairs is the same to simplify the calculation of the integrals. However, to properly account for +# all electrons, we need to duplicate the integrals for electrons with the same spin. For example, +# the :math:`pq` integral, which is the integral over the orbital :math:`p` and the orbital +# :math:`q`, can be used +# for both spin-up and spin-down electrons. Then, if we have a :math:`2 \times 2` matrix of such +# integrals, it will become a :math:`4 \times 4` matrix. The code block below simply extends the +# integrals by duplicating terms to account for both spin-up and spin-down electrons. + +for i in range(4): + if i < 2: + one = one.repeat(2, axis=i) + two = two.repeat(2, axis=i) + +############################################################################## +# We can now construct the fermionic Hamiltonian for the hydrogen molecule. The one-body terms, +# which are the first part in the Hamiltonian above, can be added first. We will use +# `itertools `_ to efficiently +# create all the combinations we need. Some of these combinations are not allowed because of spin +# restrictions and we need to exclude them. You can find more details about +# constructing a molecular Hamiltonian in reference [#surjan]_. + +import itertools + +n = one.shape[0] + +h = 0.0 + +for p, q in itertools.product(range(n), repeat=2): + if p % 2 == q % 2: # to account for spin-forbidden terms + h += one[p, q] * FermiC(p) * FermiA(q) + +############################################################################## +# The two-body terms can be added with: + +for p, q, r, s in itertools.product(range(n), repeat=4): + if p % 2 == s % 2 and q % 2 == r % 2: # to account for spin-forbidden terms + h += two[p, q, r, s] / 2 * FermiC(p) * FermiC(q) * FermiA(r) * FermiA(s) + +############################################################################## +# We then simplify the Hamiltonian to remove terms with negligible coefficients and then map it to +# the qubit basis. + +h.simplify() +h = jordan_wigner(h) + +############################################################################## +# We also need to include the contribution of the nuclear energy. + +h += np.sum(core * qml.Identity(0)) + +############################################################################## +# This gives us the qubit Hamiltonian which can be used as an input for quantum algorithms. We can +# also compute the ground-state energy by diagonalizing the matrix representation of the +# Hamiltonian in the computational basis. + +np.linalg.eigh(h.sparse_matrix().toarray())[0].min() + +############################################################################## +# Summary +# ------- +# This demo explains how to create and manipulate fermionic operators in PennyLane, which is as +# easy as writing the operators on paper. PennyLane supports several arithmetic operations between +# fermionic operators and provides tools for mapping them to the qubit basis. This makes it easy and +# intuitive to construct complicated fermionic Hamiltonians such as +# `molecular Hamiltonians `_. +# +# References +# ---------- +# +# .. [#surjan] +# +# Peter R. Surjan, "Second Quantized Approach to Quantum Chemistry". Springer-Verlag, 1989. +# +# +# About the author +# ---------------- +# .. include:: ../_static/authors/soran_jahangiri.txt diff --git a/demos_quantum-chemistry.rst b/demos_quantum-chemistry.rst index 5fe2fb4143..314bb42c4c 100644 --- a/demos_quantum-chemistry.rst +++ b/demos_quantum-chemistry.rst @@ -99,6 +99,12 @@ Quantum chemistry is one of the leading application areas of quantum computers. :description: :doc:`demos/tutorial_qchem_external` :tags: chemistry +.. gallery-item:: + :tooltip: Fermionic Operators + :figure: demonstrations/fermionic_operators/thumbnail_tutorial_fermionic_operators.png + :description: :doc:`demos/tutorial_fermionic_operators` + :tags: chemistry + :html:`

` @@ -112,6 +118,7 @@ Quantum chemistry is one of the leading application areas of quantum computers. demos/tutorial_differentiable_HF demos/tutorial_adaptive_circuits demos/tutorial_chemical_reactions + demos/tutorial_fermionic_operators demos/tutorial_mol_geo_opt demos/tutorial_vqe_spin_sectors demos/tutorial_measurement_optimize From 08206a60b44e6c1feedb2c152fa46d705fd8793e Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Fri, 23 Jun 2023 17:13:42 -0400 Subject: [PATCH 22/24] Fixes for 0.31 release (#835) * Updated ensemble demo * Trigger ci * Update circuit cutting demo * Updated stop_recording * Update tutorial to not rely on tape context * Update demonstrations/tutorial_quantum_circuit_cutting.py Co-authored-by: Matthew Silverman --------- Co-authored-by: Matthew Silverman --- demonstrations/ensemble_multi_qpu.py | 4 ++- .../tutorial_quantum_circuit_cutting.py | 35 ++++++++++--------- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/demonstrations/ensemble_multi_qpu.py b/demonstrations/ensemble_multi_qpu.py index 9430e80440..46200efa55 100644 --- a/demonstrations/ensemble_multi_qpu.py +++ b/demonstrations/ensemble_multi_qpu.py @@ -64,7 +64,9 @@ # plotting later on. The first two principal components of the data are used. np.random.seed(1967) -x, y = zip(*np.random.permutation(list(zip(x, y)))) + +data_order = np.random.permutation(np.arange(n_samples)) +x, y = x[data_order], y[data_order] pca = sklearn.decomposition.PCA(n_components=n_features) pca.fit(x) diff --git a/demonstrations/tutorial_quantum_circuit_cutting.py b/demonstrations/tutorial_quantum_circuit_cutting.py index 6ff667ef28..f6329d1461 100644 --- a/demonstrations/tutorial_quantum_circuit_cutting.py +++ b/demonstrations/tutorial_quantum_circuit_cutting.py @@ -658,28 +658,31 @@ def make_kraus_ops(num_wires: int): # In code, this looks like: # -with QuantumTape(do_queue=False) as tape0, QuantumTape(do_queue=False) as tape1: - # Record on new "fragment" tapes +cut_index = 0 +wire_cut = None - for op in tape: +for i, op in enumerate(tape.operations): + if isinstance(op, qml.WireCut): + cut_index = i + wire_cut = op + break - if isinstance(op, qml.WireCut): # If the operation is a wire cut, replace it - - k = len(op.wires) - d = 2**k +k = len(wire_cut.wires) +d = 2**k - K0, K1 = make_kraus_ops(k) # Generate Kraus operators on the fly - probs = (d + 1) / (2 * d + 1), d / (2 * d + 1) # Probabilities of the two channels +K0, K1 = make_kraus_ops(k) # Generate Kraus operators on the fly +probs = (d + 1) / (2 * d + 1), d / (2 * d + 1) # Probabilities of the two channels - psi_0 = qml.QubitChannel(K0, wires=op.wires, do_queue=False) - psi_1 = qml.QubitChannel(K1, wires=op.wires, do_queue=False) +psi_0 = qml.QubitChannel(K0, wires=wire_cut.wires) +psi_1 = qml.QubitChannel(K1, wires=wire_cut.wires) - qml.apply(psi_0, context=tape0) - qml.apply(psi_1, context=tape1) +ops_0 = tape.operations +ops_0[cut_index] = psi_0 +ops_1 = tape.operations +ops_1[cut_index] = psi_1 - else: # Otherwise, just apply the existing gate - qml.apply(op, context=tape0) - qml.apply(op, context=tape1) +tape0 = QuantumTape(ops=ops_0, measurements=tape.measurements) +tape1 = QuantumTape(ops=ops_1, measurements=tape.measurements) ###################################################################### From 9d979ee338ce532eadae78dc7978d657071542bc Mon Sep 17 00:00:00 2001 From: soranjh <40344468+soranjh@users.noreply.github.com> Date: Fri, 23 Jun 2023 17:55:30 -0400 Subject: [PATCH 23/24] Modify image in fermionic demo (#838) * use new image * Added hero_illustrationd folder to _static, added fermionic_ops_hero.png, modified tutorial_fermionic_operators.py and metadata to add hero image and delete previous image. --------- Co-authored-by: CatalinaAlbornoz --- .../hero_illustrations/fermionic_ops_hero.png | Bin 0 -> 421018 bytes .../fermionic_operators.jpg | Bin 393175 -> 0 bytes .../tutorial_fermionic_operators.metadata.json | 6 +++++- demonstrations/tutorial_fermionic_operators.py | 8 +------- 4 files changed, 6 insertions(+), 8 deletions(-) create mode 100644 _static/hero_illustrations/fermionic_ops_hero.png delete mode 100644 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"/_images/thumbnail_fermionic_operators.png" + "uri": "/_images/thumbnail_tutorial_fermionic_operators.png" }, { "type": "large_thumbnail", "uri": "/_static/large_demo_thumbnails/thumbnail_large_fermionic_operators.png" + }, + { + "type": "hero_image", + "uri": "/_static/hero_illustrations/fermionic_ops_hero.png" } ], "seoDescription": "Construct Hamiltonians and other observables using fermionic creation and annihilation operators.", diff --git a/demonstrations/tutorial_fermionic_operators.py b/demonstrations/tutorial_fermionic_operators.py index b74bf4434b..8ea0770b0b 100644 --- a/demonstrations/tutorial_fermionic_operators.py +++ b/demonstrations/tutorial_fermionic_operators.py @@ -5,7 +5,7 @@ .. meta:: :property="og:description": Learn how to work with fermionic operators - :property="og:image": https://pennylane.ai/qml/_images/fermionic_operators.jpg + :property="og:image": https://pennylane.ai/qml/_images/thumbnail_tutorial_fermionic_operators.png .. related:: tutorial_quantum_chemistry Building molecular Hamiltonians @@ -18,12 +18,6 @@ systems [#surjan]_. In this demo, you will learn how to use PennyLane to create fermionic operators and map them to a qubit representation for use in quantum algorithms. - -.. figure:: /demonstrations/fermionic_operators/fermionic_operators.jpg - :width: 60% - :align: center - - Constructing fermionic operators -------------------------------- The fermionic `creation and annihilation `_ From e82ec77510ed87a7454ccc7de658bab5b48f9c03 Mon Sep 17 00:00:00 2001 From: Matthew Silverman Date: Mon, 26 Jun 2023 18:41:03 -0400 Subject: [PATCH 24/24] use pinned versions of pennylane and plugins (#840) --- requirements.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements.txt b/requirements.txt index 1990d07b6c..d03bc26542 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,11 +15,11 @@ pyscf==2.1.1 cirq-core==0.14.1 cirq-pasqal==0.14.1 openfermionpyscf==0.5 # required by 2 tutorials: tutorial_quantum_chemistry and tutorial_adaptive_circuits and tutorial_diffable_shadows -git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane -git+https://github.com/PennyLaneAI/pennylane-cirq.git#egg=pennylane-cirq -git+https://github.com/PennyLaneAI/pennylane-qiskit.git#egg=pennylane-qiskit +pennylane==0.31.0 +pennylane-cirq==0.31.0 +pennylane-qiskit==0.31.0 qulacs==0.1.10.1 -git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs +pennylane-qulacs==0.29.0 scikit-learn==0.23.2 docutils==0.16 markupsafe==2.0.1

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