From 5619f4e26aee5a7ad07591c8b938fe69dd57418a Mon Sep 17 00:00:00 2001 From: Daniel Leidreiter <85784783+dleidreiter@users.noreply.github.com> Date: Wed, 29 May 2024 13:17:25 +0200 Subject: [PATCH] 37 implement a termination criterion for qiskit algorithms spsa (#38) * add a termination criterion for qiskit_algorithm's SPSA optimizer * update the example notebook for using the ibm runtime --- CHANGELOG.md | 2 + examples/evqe_jssp_optimization.ipynb | 156 ++++------ examples/using_the_ibm_runtime.ipynb | 274 ++++++++++-------- .../evqe/evolutionary_algorithm/individual.py | 1 - queasars/utility/spsa_termination.py | 143 +++++++++ 5 files changed, 365 insertions(+), 211 deletions(-) create mode 100644 queasars/utility/spsa_termination.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 61763a3..33f64ee 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ Current version: 0.2.0 - Add the ability to initialize EVQE individuals with more than one circuit layer ([Issue #26]) - Added the ability to use the Critival Value at Risk instead of the Expectation Value ([Issue #32]) - Added JSON serialization classes to enable the serialization of EvolvingAnsatzMinimumEigensolverResults ([Issue #35]) +- Added a termination criterion for qiskit_algorithm's SPSA optimizer ([Issue #37]) ### Fixed @@ -34,6 +35,7 @@ Current version: 0.2.0 - Initial codeless pypi commit +[Issue #37]: https://github.com/DLR-RB/QUEASARS/issues/37 [Issue #35]: https://github.com/DLR-RB/QUEASARS/issues/35 [Issue #32]: https://github.com/DLR-RB/QUEASARS/issues/32 [Issue #31]: https://github.com/DLR-RB/QUEASARS/issues/31 diff --git a/examples/evqe_jssp_optimization.ipynb b/examples/evqe_jssp_optimization.ipynb index dc77e11..20f605c 100644 --- a/examples/evqe_jssp_optimization.ipynb +++ b/examples/evqe_jssp_optimization.ipynb @@ -34,54 +34,23 @@ { "cell_type": "code", "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jssp1\n", - " Machines:\n", - " m1\n", - " m2\n", - " m3\n", - " Jobs:\n", - " j1:\n", - " j1_op1(m1, 1)\n", - " j1_op2(m2, 1)\n", - " j1_op3(m3, 1)\n", - " j2:\n", - " j2_op1(m3, 1)\n", - " j2_op2(m2, 1)\n", - " j2_op3(m1, 1)\n", - " j3:\n", - " j3_op1(m2, 1)\n", - " j3_op2(m3, 1)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from queasars.job_shop_scheduling.problem_instances import Machine, Operation, Job, JobShopSchedulingProblemInstance\n", "\n", - "machines = (Machine(name=\"m1\"), Machine(name=\"m2\"), Machine(name=\"m3\"))\n", + "machines = (Machine(name=\"m0\"), Machine(name=\"m1\"), Machine(\"m2\"))\n", "\n", - "j1op1 = Operation(name=\"op1\", job_name=\"j1\", machine=machines[0], processing_duration=1)\n", - "j1op2 = Operation(name=\"op2\", job_name=\"j1\", machine=machines[1], processing_duration=1)\n", - "j1op3 = Operation(name=\"op3\", job_name=\"j1\", machine=machines[2], processing_duration=1)\n", - "job1 = Job(name=\"j1\", operations=(j1op1, j1op2, j1op3))\n", + "j0op1 = Operation(name=\"j0op0\", machine=machines[2], processing_duration=1, job_name=\"j0\")\n", + "j0op2 = Operation(name=\"j0op1\", machine=machines[0], processing_duration=1, job_name=\"j0\")\n", + "j0op3 = Operation(name=\"j0op2\", machine=machines[1], processing_duration=2, job_name=\"j0\")\n", + "job0 = Job(name=\"j0\", operations=(j0op1, j0op2, j0op3))\n", "\n", - "j2op1 = Operation(name=\"op1\", job_name=\"j2\", machine=machines[2], processing_duration=1)\n", - "j2op2 = Operation(name=\"op2\", job_name=\"j2\", machine=machines[1], processing_duration=1)\n", - "j2op3 = Operation(name=\"op3\", job_name=\"j2\", machine=machines[0], processing_duration=1)\n", - "job2 = Job(name=\"j2\", operations=(j2op1, j2op2, j2op3))\n", - "\n", - "j3op1 = Operation(name=\"op1\", job_name=\"j3\", machine=machines[1], processing_duration=1)\n", - "j3op2 = Operation(name=\"op2\", job_name=\"j3\", machine=machines[2], processing_duration=1)\n", - "job3 = Job(name=\"j3\", operations=(j3op1, j3op2))\n", - "\n", - "jssp_instance = JobShopSchedulingProblemInstance(name=\"jssp1\", machines=machines, jobs=(job1, job2, job3))\n", + "j1op1 = Operation(name=\"j1op1\", machine=machines[2], processing_duration=2, job_name=\"j1\")\n", + "j1op2 = Operation(name=\"j1op2\", machine=machines[0], processing_duration=1, job_name=\"j1\")\n", + "j1op3 = Operation(name=\"j1op3\", machine=machines[1], processing_duration=1, job_name=\"j1\")\n", + "job1 = Job(name=\"j1\", operations=(j1op1, j1op2, j1op3))\n", "\n", - "print(jssp_instance)" + "jssp_instance = JobShopSchedulingProblemInstance(name=\"2_jobs_3_machines_seed_121\", machines=machines, jobs=(job0, job1))" ], "metadata": { "collapsed": false @@ -103,7 +72,7 @@ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -135,14 +104,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "needed qubits: 10\n" + "needed qubits: 12\n" ] } ], "source": [ "from queasars.job_shop_scheduling.domain_wall_hamiltonian_encoder import JSSPDomainWallHamiltonianEncoder\n", "\n", - "encoder = JSSPDomainWallHamiltonianEncoder(jssp_instance=jssp_instance, makespan_limit=4, max_opt_value=100, opt_all_operations_share=0.19, encoding_penalty=319, overlap_constraint_penalty=319, precedence_constraint_penalty=275)\n", + "encoder = JSSPDomainWallHamiltonianEncoder(jssp_instance=jssp_instance, makespan_limit=6, max_opt_value=100, opt_all_operations_share=0.19, encoding_penalty=319, overlap_constraint_penalty=319, precedence_constraint_penalty=275)\n", "\n", "print(\"needed qubits: \", encoder.n_qubits)\n", "\n", @@ -181,6 +150,7 @@ "from dask.distributed import LocalCluster\n", "\n", "from queasars.minimum_eigensolvers.base.termination_criteria import BestIndividualRelativeChangeTolerance\n", + "from queasars.utility.spsa_termination import SPSATerminationChecker\n", "from queasars.minimum_eigensolvers.evqe.evqe import EVQEMinimumEigensolverConfiguration\n", "\n", "# The EVQEMinimumEigensolver needs at least a sampler and can also use an estimator.\n", @@ -199,7 +169,8 @@ "# configured to terminate quickly, so that mutations are not overtly expensive.\n", "# Here we use the SPSA optimizer with a very limited amount of iterations and a\n", "# large step size.\n", - "optimizer = SPSA(maxiter=33, perturbation=0.35, learning_rate=0.43, trust_region=True, last_avg=1, resamplings=1)\n", + "termination_checker = SPSATerminationChecker(minimum_relative_change=0.01, allowed_consecutive_violations=2)\n", + "optimizer = SPSA(maxiter=33, perturbation=0.35, learning_rate=0.43, trust_region=True, last_avg=1, resamplings=1, termination_checker=termination_checker.termination_check)\n", "\n", "# To help the EVQEMinimumEigensolver deal correctly with terminations based\n", "# on the amount of circuit evaluations used, an estimate can be given for how\n", @@ -353,21 +324,21 @@ "text": [ "Starting evolution!\n", "Results for generation: 0\n", - "Current best expectation value: 195.974512\n", - "Current median expectation value: 520.687610\n", - "Current average expectation value: 694.544087\n", + "Current best expectation value: 374.915527\n", + "Current median expectation value: 659.317871\n", + "Current average expectation value: 1044.403711\n", "Results for generation: 1\n", - "Current best expectation value: 39.237500\n", - "Current median expectation value: 285.497021\n", - "Current average expectation value: 269.321477\n", + "Current best expectation value: 22.750000\n", + "Current median expectation value: 349.260824\n", + "Current average expectation value: 441.916732\n", "Results for generation: 2\n", - "Current best expectation value: 39.237500\n", - "Current median expectation value: 191.048340\n", - "Current average expectation value: 188.330281\n", + "Current best expectation value: 22.750000\n", + "Current median expectation value: 329.067057\n", + "Current average expectation value: 266.096110\n", "Results for generation: 3\n", - "Current best expectation value: 39.237500\n", - "Current median expectation value: 63.089978\n", - "Current average expectation value: 77.859556\n" + "Current best expectation value: 22.750000\n", + "Current median expectation value: 22.750000\n", + "Current average expectation value: 114.369580\n" ] } ], @@ -380,7 +351,7 @@ "logger.addHandler(handler)\n", "\n", "result = eigensolver.compute_minimum_eigenvalue(operator=hamiltonian)\n", - "quasi_distribution = result.eigenstate.binary_probabilities()\n" + "quasi_distribution = result.eigenstate.binary_probabilities()" ] }, { @@ -408,7 +379,7 @@ { "data": { "text/plain": "
", - "image/png": 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" + "image/png": 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" }, "execution_count": 8, "metadata": {}, @@ -438,47 +409,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "probability: 0.6904296875 is valid: True\n", - "jssp1 solution with makespan 4\n", + "probability: 0.6728515625 is valid: True\n", + "2_jobs_3_machines_seed_121 solution with makespan 5\n", + " j0:\n", + " j0_j0op0(m2, 1) starts at: 0 and ends at: 1\n", + " j0_j0op1(m0, 1) starts at: 1 and ends at: 2\n", + " j0_j0op2(m1, 2) starts at: 2 and ends at: 4\n", " j1:\n", - " j1_op1(m1, 1) starts at: 0 and ends at: 1\n", - " j1_op2(m2, 1) starts at: 2 and ends at: 3\n", - " j1_op3(m3, 1) starts at: 3 and ends at: 4\n", - " j2:\n", - " j2_op1(m3, 1) starts at: 0 and ends at: 1\n", - " j2_op2(m2, 1) starts at: 1 and ends at: 2\n", - " j2_op3(m1, 1) starts at: 2 and ends at: 3\n", - " j3:\n", - " j3_op1(m2, 1) starts at: 0 and ends at: 1\n", - " j3_op2(m3, 1) starts at: 1 and ends at: 2\n", + " j1_j1op1(m2, 2) starts at: 1 and ends at: 3\n", + " j1_j1op2(m0, 1) starts at: 3 and ends at: 4\n", + " j1_j1op3(m1, 1) starts at: 4 and ends at: 5\n", "\n", - "probability: 0.0849609375 is valid: False\n", - "jssp1 solution with makespan None\n", + "probability: 0.1328125 is valid: False\n", + "2_jobs_3_machines_seed_121 solution with makespan None\n", + " j0:\n", + " j0_j0op0(m2, 1) starts at: 0 and ends at: 1\n", + " j0_j0op1(m0, 1) starts at: 2 and ends at: 3\n", + " j0_j0op2(m1, 2) starts at: 2 and ends at: 4\n", " j1:\n", - " j1_op1(m1, 1) starts at: 0 and ends at: 1\n", - " j1_op2(m2, 1) starts at: 1 and ends at: 2\n", - " j1_op3(m3, 1) starts at: 3 and ends at: 4\n", - " j2:\n", - " j2_op1(m3, 1) starts at: 0 and ends at: 1\n", - " j2_op2(m2, 1) starts at: 1 and ends at: 2\n", - " j2_op3(m1, 1) starts at: 2 and ends at: 3\n", - " j3:\n", - " j3_op1(m2, 1) starts at: 0 and ends at: 1\n", - " j3_op2(m3, 1) starts at: 1 and ends at: 2\n", + " j1_j1op1(m2, 2) starts at: 1 and ends at: 3\n", + " j1_j1op2(m0, 1) starts at: 3 and ends at: 4\n", + " j1_j1op3(m1, 1) starts at: 4 and ends at: 5\n", "\n", - "probability: 0.0908203125 is valid: False\n", - "jssp1 solution with makespan None\n", + "probability: 0.068359375 is valid: False\n", + "2_jobs_3_machines_seed_121 solution with makespan None\n", + " j0:\n", + " j0_j0op0(m2, 1) starts at: 0 and ends at: 1\n", + " j0_j0op1(m0, 1) starts at: 1 and ends at: 2\n", + " j0_j0op2(m1, 2) starts at: 2 and ends at: 4\n", " j1:\n", - " j1_op1(m1, 1) starts at: 0 and ends at: 1\n", - " j1_op2(m2, 1) starts at: 2 and ends at: 3\n", - " j1_op3(m3, 1) starts at: 2 and ends at: 3\n", - " j2:\n", - " j2_op1(m3, 1) starts at: 0 and ends at: 1\n", - " j2_op2(m2, 1) starts at: 1 and ends at: 2\n", - " j2_op3(m1, 1) starts at: 2 and ends at: 3\n", - " j3:\n", - " j3_op1(m2, 1) starts at: 0 and ends at: 1\n", - " j3_op2(m3, 1) starts at: 1 and ends at: 2\n", + " j1_j1op1(m2, 2) starts at: 0 and ends at: 2\n", + " j1_j1op2(m0, 1) starts at: 3 and ends at: 4\n", + " j1_j1op3(m1, 1) starts at: 4 and ends at: 5\n", "\n" ] } @@ -516,7 +478,7 @@ { "data": { "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" diff --git a/examples/using_the_ibm_runtime.ipynb b/examples/using_the_ibm_runtime.ipynb index 6065b16..b0d7d2b 100644 --- a/examples/using_the_ibm_runtime.ipynb +++ b/examples/using_the_ibm_runtime.ipynb @@ -9,8 +9,8 @@ "outputs_hidden": true }, "ExecuteTime": { - "end_time": "2024-02-17T19:53:51.862187700Z", - "start_time": "2024-02-17T19:53:51.825497200Z" + "end_time": "2024-05-29T08:45:09.178947Z", + "start_time": "2024-05-29T08:45:09.154644700Z" } }, "outputs": [], @@ -35,48 +35,27 @@ { "cell_type": "code", "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jssp1\n", - " Machines:\n", - " m1\n", - " m2\n", - " Jobs:\n", - " j1:\n", - " j1_j1op1(m1, 1)\n", - " j1_j1op2(m2, 1)\n", - " j2:\n", - " j2_j2op1(m1, 1)\n", - " j2_j2op2(m2, 1)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from queasars.job_shop_scheduling.problem_instances import Machine, Operation, Job, JobShopSchedulingProblemInstance\n", "\n", - "machines = (Machine(name=\"m1\"), Machine(name=\"m2\"))\n", + "machines = (Machine(name=\"m0\"), Machine(name=\"m1\"))\n", + "\n", + "j0op1 = Operation(name=\"j0op0\", machine=machines[0], processing_duration=2, job_name=\"j0\")\n", + "j0op2 = Operation(name=\"j0op1\", machine=machines[1], processing_duration=1, job_name=\"j0\")\n", + "job0 = Job(name=\"j0\", operations=(j0op1, j0op2))\n", "\n", "j1op1 = Operation(name=\"j1op1\", machine=machines[0], processing_duration=1, job_name=\"j1\")\n", - "j1op2 = Operation(name=\"j1op2\", machine=machines[1], processing_duration=1, job_name=\"j1\")\n", + "j1op2 = Operation(name=\"j1op2\", machine=machines[1], processing_duration=2, job_name=\"j1\")\n", "job1 = Job(name=\"j1\", operations=(j1op1, j1op2))\n", "\n", - "j2op1 = Operation(name=\"j2op1\", machine=machines[0], processing_duration=1, job_name=\"j2\")\n", - "j2op2 = Operation(name=\"j2op2\", machine=machines[1], processing_duration=1, job_name=\"j2\")\n", - "job2 = Job(name=\"j2\", operations=(j2op1, j2op2))\n", - "\n", - "jssp_instance = JobShopSchedulingProblemInstance(name=\"jssp1\", machines=machines, jobs=(job1, job2))\n", - "\n", - "print(jssp_instance)" + "jssp_instance = JobShopSchedulingProblemInstance(name=\"Simple Instance\", machines=machines, jobs=(job0, job1))" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T19:53:51.885787Z", - "start_time": "2024-02-17T19:53:51.866185200Z" + "end_time": "2024-05-29T08:45:09.199946500Z", + "start_time": "2024-05-29T08:45:09.160667300Z" } } }, @@ -96,16 +75,7 @@ { "data": { "text/plain": "
", - "image/png": 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" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -114,13 +84,13 @@ "source": [ "from queasars.job_shop_scheduling.visualization import plot_jssp_problem_instance_gantt\n", "\n", - "plot_jssp_problem_instance_gantt(problem_instance=jssp_instance)" + "plot = plot_jssp_problem_instance_gantt(problem_instance=jssp_instance)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T19:53:52.643738500Z", - "start_time": "2024-02-17T19:53:51.887782700Z" + "end_time": "2024-05-29T08:45:09.851875200Z", + "start_time": "2024-05-29T08:45:09.174947600Z" } } }, @@ -141,14 +111,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "needed qubits: 4\n" + "needed qubits: 8\n" ] } ], "source": [ "from queasars.job_shop_scheduling.domain_wall_hamiltonian_encoder import JSSPDomainWallHamiltonianEncoder\n", "\n", - "encoder = JSSPDomainWallHamiltonianEncoder(jssp_instance=jssp_instance, makespan_limit=3, max_opt_value=100, opt_all_operations_share=0.19, encoding_penalty=319, overlap_constraint_penalty=319, precedence_constraint_penalty=275)\n", + "encoder = JSSPDomainWallHamiltonianEncoder(jssp_instance=jssp_instance, makespan_limit=5, opt_all_operations_share=0.19, max_opt_value=100, encoding_penalty=319, overlap_constraint_penalty=319, precedence_constraint_penalty=275)\n", "\n", "print(\"needed qubits: \", encoder.n_qubits)\n", "\n", @@ -157,8 +127,42 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T19:53:53.214089300Z", - "start_time": "2024-02-17T19:53:52.633736800Z" + "end_time": "2024-05-29T08:45:10.390879900Z", + "start_time": "2024-05-29T08:45:09.846864600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "from qiskit import QuantumCircuit\n", + "from qiskit.primitives import SamplerResult\n", + "from qiskit.primitives.base import BaseSamplerV1\n", + "from qiskit.primitives.primitive_job import PrimitiveJob\n", + "from qiskit.transpiler import PassManager\n", + "\n", + "# Sampler Primitive Wrapper to adjust for qiskit_ibm_runtime_primitives no longer \n", + "# offering cloud transpilation for free\n", + "class TranspilingSampler(BaseSamplerV1):\n", + " def __init__(self, sampler: BaseSamplerV1, pass_manager: PassManager):\n", + " super().__init__()\n", + " self._sampler = sampler\n", + " self._pass_manager = pass_manager\n", + "\n", + " def _run(\n", + " self, circuits: tuple[QuantumCircuit, ...], parameter_values: tuple[tuple[float, ...], ...], **run_options\n", + " ) -> PrimitiveJob[SamplerResult]:\n", + " applied_circuits = [circuit.assign_parameters(params) for circuit, params in zip(circuits, parameter_values)]\n", + " transpiled_circuits = self._pass_manager.run(applied_circuits)\n", + " return self._sampler.run(transpiled_circuits, **run_options)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-29T08:45:10.420286600Z", + "start_time": "2024-05-29T08:45:10.389163300Z" } } }, @@ -173,12 +177,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T20:06:14.969805900Z", - "start_time": "2024-02-17T19:53:53.218868600Z" + "end_time": "2024-05-29T08:46:11.519342900Z", + "start_time": "2024-05-29T08:45:10.425284Z" } }, "outputs": [ @@ -186,51 +190,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\leid_dn\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\queasars-S3kMH3lw-py3.11\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n", + "C:\\Users\\shark\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\queasars-jkqbWnG0-py3.11\\Lib\\site-packages\\qiskit_ibm_runtime\\session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n", " warnings.warn(\n", - "C:\\Users\\leid_dn\\AppData\\Local\\Temp\\ipykernel_3276\\708447267.py:24: DeprecationWarning: The Sampler and Estimator V1 primitives have been deprecated as of qiskit-ibm-runtime 0.23.0 and will be removed no sooner than 3 months after the release date. Please use the V2 Primitives. See the `V2 migration guide `_. for more details\n", - " sampler = Sampler(session=session, options={\"shots\": 512, \"optimization_level\": 0, \"resilience_level\": 0})\n", + "C:\\Users\\shark\\AppData\\Local\\Temp\\ipykernel_4900\\2634276111.py:28: DeprecationWarning: The Sampler and Estimator V1 primitives have been deprecated as of qiskit-ibm-runtime 0.23.0 and will be removed no sooner than 3 months after the release date. Please use the V2 Primitives. See the `V2 migration guide `_. for more details\n", + " sampler = Sampler(session=batch, options={\"shots\": 512, \"optimization_level\": 0, \"resilience_level\": 0})\n", "Starting evolution!\n", "Results for generation: 0\n", - "Current best expectation value: 63.500000\n", - "Current median expectation value: 168.567871\n", - "Current average expectation value: 277.935938\n", + "Current best expectation value: 37.065972\n", + "Current median expectation value: 122.222222\n", + "Current average expectation value: 110.460069\n", "Results for generation: 1\n", - "Current best expectation value: 63.500000\n", - "Current median expectation value: 63.500000\n", - "Current average expectation value: 63.500000\n", + "Current best expectation value: 22.222222\n", + "Current median expectation value: 22.222222\n", + "Current average expectation value: 44.696181\n", "Results for generation: 2\n", - "Current best expectation value: 63.500000\n", - "Current median expectation value: 63.500000\n", - "Current average expectation value: 63.500000\n" + "Current best expectation value: 22.222222\n", + "Current median expectation value: 22.222222\n", + "Current average expectation value: 22.430556\n" ] } ], "source": [ "import logging\n", "\n", - "from qiskit_aer import AerSimulator\n", "from qiskit_algorithms.optimizers import SPSA\n", - "from qiskit_ibm_runtime import QiskitRuntimeService, Session, Sampler\n", + "from qiskit_ibm_runtime import QiskitRuntimeService, Batch, Sampler\n", + "from qiskit_ibm_runtime.fake_provider.backends import FakeOsaka\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", "\n", - "from queasars.minimum_eigensolvers.base.termination_criteria import BestIndividualRelativeChangeTolerance\n", + "from queasars.utility.spsa_termination import SPSATerminationChecker\n", "from queasars.minimum_eigensolvers.evqe.evqe import EVQEMinimumEigensolverConfiguration, EVQEMinimumEigensolver\n", "\n", - "# Connect to the runtime service using your IBM quantum token.\n", + "# Connect to the runtime service using your IBM quantum token\n", "runtime_service = QiskitRuntimeService(channel=\"ibm_quantum\", token=\"Your token here!\")\n", "\n", - "# Set this to the string name of the ibm quantum backend if you want to use real quantum hardware.\n", - "backend = AerSimulator()\n", + "# Uncomment the second line and replace the backend name accordingly if you want to use real Quantum Hardware!\n", + "backend = FakeOsaka()\n", + "#backend = runtime_service.backend(\"ibm_kyoto\")\n", + "\n", + "# setup a pass manager to setup the transpilation process\n", + "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", "\n", "# Set up a session so that consecutive jobs may run in short succession.\n", - "# For this purpose a backend needs to be set. In this example the ibmq_qasm_simulator suffices.\n", - "# But one could also specify a real quantum backend for example like ibm_osaka.\n", - "with Session(service=runtime_service, backend=backend) as session:\n", + "with Batch(service=runtime_service, backend=backend) as batch:\n", " \n", " # The EVQEMinimumEigensolver needs at least a sampler and can also use an estimator.\n", " # Here we only use a sampler, as the Critical Value at Risk objective value can\n", " # can only be used when only using the sampler.\n", - " sampler = Sampler(session=session, options={\"shots\": 512, \"optimization_level\": 0, \"resilience_level\": 0})\n", + " sampler = Sampler(session=batch, options={\"shots\": 512, \"optimization_level\": 0, \"resilience_level\": 0})\n", + " sampler = TranspilingSampler(sampler, pass_manager)\n", + " estimator = None\n", "\n", " # If only a sampler is used, the expectation value with respect to the Hamiltonian\n", " # is calculated using the measurement distribution provided by the sampler. This\n", @@ -242,44 +251,47 @@ " # configured to terminate quickly, so that mutations are not overtly expensive.\n", " # Here we use the SPSA optimizer with a very limited amount of iterations and a\n", " # large step size.\n", - " optimizer = SPSA(maxiter=33, perturbation=0.35, learning_rate=0.43, trust_region=True, last_avg=1, resamplings=1)\n", + " termination_checker = SPSATerminationChecker(minimum_relative_change=0.01, allowed_consecutive_violations=2)\n", + " optimizer = SPSA(maxiter=33, perturbation=0.35, learning_rate=0.43, trust_region=True, last_avg=1, resamplings=1, termination_checker=termination_checker.termination_check)\n", " \n", " # To help the EVQEMinimumEigensolver deal correctly with terminations based\n", " # on the amount of circuit evaluations used, an estimate can be given for how\n", " # many circuit evaluations the optimizer uses per optimization run.\n", + " # SPSA makes two measurements per gradient approximation, which means in total it will\n", + " # need 66 circuit evaluations for 33 iterations.\n", " optimizer_n_circuit_evaluations = 66\n", " \n", " # To specify when the EVQEMinimumEigensolver should terminate either max_generations,\n", " # max_circuit_evaluations or a termination_criterion should be given.\n", " # Here we set a fixed generation limit, to reduce the runtime usage.\n", - " max_generations = 5\n", + " max_generations = 3\n", " max_circuit_evaluations = None\n", - " termination_criterion = BestIndividualRelativeChangeTolerance(minimum_relative_change=0.01, allowed_consecutive_violations=1)\n", + " termination_criterion = None\n", " \n", " # A random seed can be provided to control the randomness of the evolutionary process.\n", - " random_seed = 0\n", + " random_seed = None\n", " \n", " # The population size determines how many individuals are evaluated each generation.\n", " # With a higher population size, fewer generations might be needed, but this also\n", " # makes each generation more expensive to evaluate.\n", - " population_size = 10\n", + " population_size = 5\n", " \n", " # The initial individuals in the starting population can be initialized with\n", " # an arbitrary amount of layers and fully randomized parameter values. This\n", " # can be particularly useful if the state |0 .. 0> is a local minima and\n", " # the individuals should not start in that state\n", - " n_initial_layers = 2\n", + " n_initial_layers = 1\n", " randomize_initial_parameter_values = True\n", " \n", " # Determines how many circuit layers apart two individuals need to be, to be considered to\n", - " # be of a different species. Reasonable values might be in the range 2 - 5.\n", + " # be of a different species. Reasonable values might be in the range 1 - 5.\n", " speciation_genetic_distance_threshold = 1\n", "\n", " # This implementation of EVQE offers both roulette wheel selection and tournament selection.\n", " # Since tournament selection is more robust, we use it here.\n", " use_tournament_selection = True\n", " tournament_size = 2\n", - " \n", + "\n", " # The alpha and beta penalties penalize quantum circuits of increasing depth (alpha) and\n", " # increasing amount of controlled rotations (beta). increase them if the quantum circuits get to\n", " # deep or complicated.\n", @@ -292,12 +304,11 @@ " \n", " # The topological search probability determines how likely a circuit layer is added to an individual\n", " # as a mutation. This should be a higher probability to encourage exploration of different circuits.\n", - " # Here we will use a likelihood of 0.5\n", " topological_search_probability = 0.79\n", " \n", " # The layer removal probability determines how likely circuit layers are removed from an individual\n", " # as a mutation. This is a very disruptive mutation and should only be used sparingly to counteract\n", - " # circuit growth. Here we will use a probability of 0.1\n", + " # circuit growth.\n", " layer_removal_probability = 0.02\n", " \n", " # An executor for launching parallel computation can be specified.\n", @@ -315,7 +326,7 @@ " \n", " configuration = EVQEMinimumEigensolverConfiguration(\n", " sampler=sampler,\n", - " estimator=None,\n", + " estimator=estimator,\n", " distribution_alpha_tail=distribution_alpha_tail,\n", " optimizer=optimizer,\n", " optimizer_n_circuit_evaluations=optimizer_n_circuit_evaluations,\n", @@ -348,6 +359,35 @@ " result = eigensolver.compute_minimum_eigenvalue(operator=hamiltonian)" ] }, + { + "cell_type": "markdown", + "source": [ + "# Serialize and store the results" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "from json import dump\n", + "from queasars.minimum_eigensolvers.base.serialization import EvolvingAnsatzMinimumEigensolverResultJSONEncoder\n", + "\n", + "file_path = Path(main_directory, \"examples\", \"queasars_result.json\")\n", + "with open(file_path, \"w\") as file:\n", + " dump(obj=result, fp=file, indent=2, cls=EvolvingAnsatzMinimumEigensolverResultJSONEncoder)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-05-29T08:46:11.964091500Z", + "start_time": "2024-05-29T08:46:11.521343300Z" + } + }, + "execution_count": 7 + }, { "cell_type": "markdown", "metadata": { @@ -359,21 +399,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T20:08:40.254938300Z", - "start_time": "2024-02-17T20:08:40.149703500Z" + "end_time": "2024-05-29T08:46:12.144867700Z", + "start_time": "2024-05-29T08:46:11.965805100Z" } }, "outputs": [ { "data": { "text/plain": "
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" 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" }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -396,30 +436,38 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "probability: 0.775390625\n", - "jssp1 solution with makespan 3\n", + "probability: 0.482421875\n", + "Simple Instance solution with makespan 4\n", + " j0:\n", + " j0_j0op0(m0, 2) starts at: 1 and ends at: 3\n", + " j0_j0op1(m1, 1) starts at: 3 and ends at: 4\n", " j1:\n", - " j1_j1op1(m1, 1) starts at: 1 and ends at: 2\n", - " j1_j1op2(m2, 1) starts at: 2 and ends at: 3\n", - " j2:\n", - " j2_j2op1(m1, 1) starts at: 0 and ends at: 1\n", - " j2_j2op2(m2, 1) starts at: 1 and ends at: 2\n", + " j1_j1op1(m0, 1) starts at: 0 and ends at: 1\n", + " j1_j1op2(m1, 2) starts at: 1 and ends at: 3\n", "\n", - "probability: 0.1796875\n", - "jssp1 solution with makespan None\n", + "probability: 0.130859375\n", + "Simple Instance solution with makespan None\n", + " j0:\n", + " j0_j0op0(m0, 2) starts at: 0 and ends at: 2\n", + " j0_j0op1(m1, 1) starts at: 3 and ends at: 4\n", " j1:\n", - " j1_j1op1(m1, 1) starts at: 1 and ends at: 2\n", - " j1_j1op2(m2, 1) starts at: 2 and ends at: 3\n", - " j2:\n", - " j2_j2op1(m1, 1) starts at: 0 and ends at: 1\n", - " j2_j2op2(m2, 1) starts at: 2 and ends at: 3\n", - "\n" + " j1_j1op1(m0, 1) starts at: 0 and ends at: 1\n", + " j1_j1op2(m1, 2) starts at: 1 and ends at: 3\n", + "\n", + "probability: 0.181640625\n", + "Simple Instance solution with makespan None\n", + " j0:\n", + " j0_j0op0(m0, 2) starts at: 1 and ends at: 3\n", + " j0_j0op1(m1, 1) starts at: 2 and ends at: 3\n", + " j1:\n", + " j1_j1op1(m0, 1) starts at: 0 and ends at: 1\n", + " j1_j1op2(m1, 2) starts at: 1 and ends at: 3\n" ] } ], @@ -438,8 +486,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T20:06:15.117030900Z", - "start_time": "2024-02-17T20:06:15.110312Z" + "end_time": "2024-05-29T08:46:12.145872200Z", + "start_time": "2024-05-29T08:46:12.141962200Z" } } }, @@ -454,12 +502,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "outputs": [ { "data": { "text/plain": "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -475,8 +523,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-17T20:06:15.349133100Z", - "start_time": "2024-02-17T20:06:15.114036800Z" + "end_time": "2024-05-29T08:46:12.244516900Z", + "start_time": "2024-05-29T08:46:12.145872200Z" } } } diff --git a/queasars/minimum_eigensolvers/evqe/evolutionary_algorithm/individual.py b/queasars/minimum_eigensolvers/evqe/evolutionary_algorithm/individual.py index 1b2ec4e..73eefda 100644 --- a/queasars/minimum_eigensolvers/evqe/evolutionary_algorithm/individual.py +++ b/queasars/minimum_eigensolvers/evqe/evolutionary_algorithm/individual.py @@ -316,7 +316,6 @@ def get_partially_parameterized_quantum_circuit(self, parameterized_layers: set[ ) gate = layer.get_layer_gate(layer_id=i, parameter_values=layer_parameter_values) circuit.append(instruction=gate, qargs=range(0, n_qubits)) - circuit.barrier() return circuit def get_parameter_values(self) -> tuple[float, ...]: diff --git a/queasars/utility/spsa_termination.py b/queasars/utility/spsa_termination.py new file mode 100644 index 0000000..9aa6975 --- /dev/null +++ b/queasars/utility/spsa_termination.py @@ -0,0 +1,143 @@ +# Quantum Evolving Ansatz Variational Solver (QUEASARS) +# Copyright 2024 DLR - Deutsches Zentrum für Luft- und Raumfahrt e.V. + +from time import time +from typing import Optional +from numpy.typing import NDArray +import logging +from sys import stdout + + +class SPSATerminationChecker: + """ + Termination checker for qiskit_algorithms SPSA optimizer. For each iteration, it checks that the + absolute difference between the last and new function value, divided by the last function value, + does not fall below minimum_relative_change for more than allowed_consecutive_violations times. + This termination checker also keeps track of the best function value and parameter values + seen during the optimization, as well as the entire history of observed function values. + + :param minimum_relative_change: threshold value for the change in function value relative to the last + function value, below which this termination checker chooses to terminate the SPSA optimization + :type minimum_relative_change: float + :param allowed_consecutive_violations: determines how often the threshold value can be violated consecutively + before this termination checker chooses to terminate. If set to 0, this terminates the first time the change + falls below the threshold value. If set to 2 for example, this terminates the first time the change falls below + the threshold three consecutive times. Must be at least 0. + :type allowed_consecutive_violations: int + :param maxfev: maximum amount of function evaluations the SPSA optimizer may utilize + :type maxfev: Optional[int] + """ + + def __init__( + self, + minimum_relative_change: float, + allowed_consecutive_violations: int, + maxfev: Optional[int] = None, + ): + self._minimum_relative_change: float = minimum_relative_change + self._allowed_consecutive_violations: int = allowed_consecutive_violations + self._maxfev: Optional[int] = maxfev + self._function_value_history: list[float] = [] + self._change_history: list[float] = [] + self._n_function_evaluations = 0 + self._n_function_evaluation_history: list[float] = [] + self._best_function_value: float = float("inf") + self._best_parameter_values: Optional[NDArray] = None + self._done: bool = False + + def termination_check( + self, + n_function_evaluations: int, + parameter_values: NDArray, + function_value: float, + step_size: float, + accepted: bool, + ) -> bool: + """Given the callback values provided by qiskit_algorithm's SPSA optimizer, this method determines + whether the SPSA optimization should terminate""" + + if self._done or n_function_evaluations < self._n_function_evaluations: + self._function_value_history = [] + self._change_history = [] + self._n_function_evaluations = 0 + self._n_function_evaluation_history = [] + self._best_function_value = float("inf") + self._best_parameter_values = None + self._done = False + + self._n_function_evaluations = n_function_evaluations + + if self._maxfev is not None and self._n_function_evaluations >= self._maxfev: + return True + + if not accepted: + return False + + self._function_value_history.append(function_value) + self._n_function_evaluation_history.append(n_function_evaluations) + + if function_value < self._best_function_value: + self._best_function_value = function_value + self._best_parameter_values = parameter_values + + if len(self._function_value_history) < 2: + return False + + change = abs(function_value - self._function_value_history[-2]) / self._function_value_history[-2] + self._change_history.append(change) + + if len(self._change_history) < self._allowed_consecutive_violations + 1: + return False + + if max(self._change_history[-self._allowed_consecutive_violations - 1 :]) < self._minimum_relative_change: + self._done = True + return True + + return False + + @property + def n_function_evaluations(self) -> int: + """ + :return: the amount of function evaluations used by SPSA until termination + :rtype: int + """ + return self._n_function_evaluations + + @property + def function_value_history(self) -> list[float]: + """ + :return: a list of all encountered function values + :rtype: list[float] + """ + return self._function_value_history + + @property + def n_function_evaluation_history(self) -> list[float]: + """ + :return: a list of function evaluations needed of the same length as function_value_history + :rtype: list[float] + """ + return self._n_function_evaluation_history + + @property + def best_function_value(self) -> float: + """ + :return: the best function value encountered during the optimization + :rtype: float + """ + return self._best_function_value + + @property + def best_parameter_values(self) -> NDArray: + """ + :return: the best parameter values found during the optimization + :rtype: NDArray + :raises: ValueError, if termination_check was never called + """ + if self._best_parameter_values is None: + raise ValueError( + "The termination checker seems to have never been called! Therefore it currently" + + "stores no parameter values!" + ) + + return self._best_parameter_values