comments

docs/qml/index.md

@@ -22,14 +22,15 @@ from sympy import acos
 
 n_qubits = 4
 
+# Example feature map, also directly available with the `feature_map` function
 fp = qd.FeatureParameter("phi")
-feature_map = qd.kron(RX(i, 2 * acos(fp)) for i in range(n_qubits))
+fm = qd.kron(RX(i, acos(fp)) for i in range(n_qubits))
 
 # the key in the dictionary must correspond to
 # the name of the assigned to the feature parameter
 inputs = {"phi": torch.rand(3)}
-samples = qd.sample(feature_map, values=inputs)
-print(samples[0])
+samples = qd.sample(fm, values=inputs)
+print(samples[0])  # markdown-exec: hide
 ```
 
 The [`constructors.feature_map`][qadence.constructors.feature_map] module provides
@@ -64,7 +65,7 @@ values = {"phi": torch.rand(10, requires_grad=True)}
 # the forward pass of the quantum model returns the expectation
 # value of the input observable
 out = model(values)
-print(f"Quantum model output: \n{out}\n")
+print(f"Quantum model output: \n{out}\n")  # markdown-exec: hide
 
 # you can compute the gradient with respect to inputs using
 # PyTorch autograd differentiation engine

docs/qml/ml_tools.md

@@ -50,8 +50,8 @@ for i in range(n_epochs):
 ## Optimization routines
 
 For training QML models, Qadence also offers a few out-of-the-box routines for optimizing differentiable
-models like `QNN`s and `QuantumModel`s containing either *trainable* and/or *non-trainable* parameters
-(you can refer to [the parameters tutorial](../tutorials/parameters.md) for a refresh about different parameter types):
+models, _e.g._ `QNN`s and `QuantumModel`s containing either *trainable* and/or *non-trainable* parameters
+(see [the parameters tutorial](../tutorials/parameters.md) for a refresh about different parameter types):
 
 * [`train_with_grad`][qadence.ml_tools.train_with_grad] for gradient-based optimization using PyTorch native optimizers
 * [`train_gradient_free`][qadence.ml_tools.train_gradient_free] for gradient-free optimization using

docs/qml/qml_constructors.md

@@ -145,7 +145,7 @@ from qadence.draw import html_string # markdown-exec: hide
 print(html_string(ansatz, size="8,4")) # markdown-exec: hide
 ```
 
-Having a truly *hardware-efficient* ansatz means that the entangling operation can be chosen according to each device's native interactions. Besides digital operations, in Qadence it is also possible to build digital-analog HEAs with the entanglement produced by the natural evolution of a set of interacting qubits, as natively implemented in neutral atom devices. As with other digital-analog functions, this can be controlled with the `strategy` argument which can be chosen from the [`Strategy`](../qadence/types.md) enum type. Currently, only `Strategy.DIGITAL` and `Strategy.SDAQC` are available. By default, calling `strategy = Strategy.SDAQC` will use a global entangling Hamiltonian with Ising-like NN interactions and constant interaction strength,
+Having a truly *hardware-efficient* ansatz means that the entangling operation can be chosen according to each device's native interactions. Besides digital operations, in Qadence it is also possible to build digital-analog HEAs with the entanglement produced by the natural evolution of a set of interacting qubits, as natively implemented in neutral atom devices. As with other digital-analog functions, this can be controlled with the `strategy` argument which can be chosen from the [`Strategy`](../qadence/types.md) enum type. Currently, only `Strategy.DIGITAL` and `Strategy.SDAQC` are available. By default, calling `strategy = Strategy.SDAQC` will use a global entangling Hamiltonian with Ising-like $NN$ interactions and constant interaction strength,
 
 ```python exec="on" source="material-block" html="1" session="ansatz"
 from qadence import Strategy

mkdocs.yml

@@ -28,12 +28,10 @@ nav:
 
   - Variational quantum algorithms:
     - qml/index.md
-    - Tools for quantum machine learning:
-      - Constructors: qml/qml_constructors.md
-      - Training tools: qml/ml_tools.md
-    - Example applications:
-      - Quantum circuit learning: qml/qcl.md
-      - Solving MaxCut with QAOA: qml/qaoa.md
+    - Constructors: qml/qml_constructors.md
+    - Training tools: qml/ml_tools.md
+    - Quantum circuit learning: qml/qcl.md
+    - Solving MaxCut with QAOA: qml/qaoa.md
 
   - Advanced Tutorials:
     - Quantum circuits differentiation: advanced_tutorials/differentiability.md