[WIP] add Quantum Kernel and Quantum Kernel Training benchmarks

asv.conf.json

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+{
+    // The version of the config file format.  Do not change, unless
+    // you know what you are doing.
+    "version": 1,
+
+    // The name of the project being benchmarked
+    "project": "project",
+
+    // The project's homepage
+    "project_url": "http://project-homepage.org/",
+
+    // The URL or local path of the source code repository for the
+    // project being benchmarked
+    "repo": ".",
+
+    // The Python project's subdirectory in your repo.  If missing or
+    // the empty string, the project is assumed to be located at the root
+    // of the repository.
+    // "repo_subdir": "",
+
+    // Customizable commands for building, installing, and
+    // uninstalling the project. See asv.conf.json documentation.
+    //
+    // "install_command": ["in-dir={env_dir} python -mpip install {wheel_file}"],
+    // "uninstall_command": ["return-code=any python -mpip uninstall -y {project}"],
+    // "build_command": [
+    //     "python setup.py build",
+    //     "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}"
+    // ],
+
+    // List of branches to benchmark. If not provided, defaults to "master"
+    // (for git) or "default" (for mercurial).
+    // "branches": ["master"], // for git
+    // "branches": ["default"],    // for mercurial
+
+    // The DVCS being used.  If not set, it will be automatically
+    // determined from "repo" by looking at the protocol in the URL
+    // (if remote), or by looking for special directories, such as
+    // ".git" (if local).
+    // "dvcs": "git",
+
+    // The tool to use to create environments.  May be "conda",
+    // "virtualenv" or other value depending on the plugins in use.
+    // If missing or the empty string, the tool will be automatically
+    // determined by looking for tools on the PATH environment
+    // variable.
+    "environment_type": "virtualenv",
+
+    // timeout in seconds for installing any dependencies in environment
+    // defaults to 10 min
+    //"install_timeout": 600,
+
+    // the base URL to show a commit for the project.
+    // "show_commit_url": "http://github.com/owner/project/commit/",
+
+    // The Pythons you'd like to test against.  If not provided, defaults
+    // to the current version of Python used to run `asv`.
+    // "pythons": ["2.7", "3.6"],
+
+    // The list of conda channel names to be searched for benchmark
+    // dependency packages in the specified order
+    // "conda_channels": ["conda-forge", "defaults"],
+
+    // A conda environment file that is used for environment creation.
+    // "conda_environment_file": "environment.yml",
+
+    // The matrix of dependencies to test.  Each key of the "req"
+    // requirements dictionary is the name of a package (in PyPI) and
+    // the values are version numbers.  An empty list or empty string
+    // indicates to just test against the default (latest)
+    // version. null indicates that the package is to not be
+    // installed. If the package to be tested is only available from
+    // PyPi, and the 'environment_type' is conda, then you can preface
+    // the package name by 'pip+', and the package will be installed
+    // via pip (with all the conda available packages installed first,
+    // followed by the pip installed packages).
+    //
+    // The ``@env`` and ``@env_nobuild`` keys contain the matrix of
+    // environment variables to pass to build and benchmark commands.
+    // An environment will be created for every combination of the
+    // cartesian product of the "@env" variables in this matrix.
+    // Variables in "@env_nobuild" will be passed to every environment
+    // during the benchmark phase, but will not trigger creation of
+    // new environments.  A value of ``null`` means that the variable
+    // will not be set for the current combination.
+    //
+    // "matrix": {
+    //     "req": {
+    //         "numpy": ["1.6", "1.7"],
+    //         "six": ["", null],  // test with and without six installed
+    //         "pip+emcee": [""]   // emcee is only available for install with pip.
+    //     },
+    //     "env": {"ENV_VAR_1": ["val1", "val2"]},
+    //     "env_nobuild": {"ENV_VAR_2": ["val3", null]},
+    // },
+
+    // Combinations of libraries/python versions can be excluded/included
+    // from the set to test. Each entry is a dictionary containing additional
+    // key-value pairs to include/exclude.
+    //
+    // An exclude entry excludes entries where all values match. The
+    // values are regexps that should match the whole string.
+    //
+    // An include entry adds an environment. Only the packages listed
+    // are installed. The 'python' key is required. The exclude rules
+    // do not apply to includes.
+    //
+    // In addition to package names, the following keys are available:
+    //
+    // - python
+    //     Python version, as in the *pythons* variable above.
+    // - environment_type
+    //     Environment type, as above.
+    // - sys_platform
+    //     Platform, as in sys.platform. Possible values for the common
+    //     cases: 'linux2', 'win32', 'cygwin', 'darwin'.
+    // - req
+    //     Required packages
+    // - env
+    //     Environment variables
+    // - env_nobuild
+    //     Non-build environment variables
+    //
+    // "exclude": [
+    //     {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows
+    //     {"environment_type": "conda", "req": {"six": null}}, // don't run without six on conda
+    //     {"env": {"ENV_VAR_1": "val2"}}, // skip val2 for ENV_VAR_1
+    // ],
+    //
+    // "include": [
+    //     // additional env for python2.7
+    //     {"python": "2.7", "req": {"numpy": "1.8"}, "env_nobuild": {"FOO": "123"}},
+    //     // additional env if run on windows+conda
+    //     {"platform": "win32", "environment_type": "conda", "python": "2.7", "req": {"libpython": ""}},
+    // ],
+
+    // The directory (relative to the current directory) that benchmarks are
+    // stored in.  If not provided, defaults to "benchmarks"
+    // "benchmark_dir": "benchmarks",
+
+    // The directory (relative to the current directory) to cache the Python
+    // environments in.  If not provided, defaults to "env"
+    "env_dir": ".asv/env",
+
+    // The directory (relative to the current directory) that raw benchmark
+    // results are stored in.  If not provided, defaults to "results".
+    "results_dir": ".asv/results",
+
+    // The directory (relative to the current directory) that the html tree
+    // should be written to.  If not provided, defaults to "html".
+    "html_dir": ".asv/html",
+
+    // The number of characters to retain in the commit hashes.
+    // "hash_length": 8,
+
+    // `asv` will cache results of the recent builds in each
+    // environment, making them faster to install next time.  This is
+    // the number of builds to keep, per environment.
+    // "build_cache_size": 2,
+
+    // The commits after which the regression search in `asv publish`
+    // should start looking for regressions. Dictionary whose keys are
+    // regexps matching to benchmark names, and values corresponding to
+    // the commit (exclusive) after which to start looking for
+    // regressions.  The default is to start from the first commit
+    // with results. If the commit is `null`, regression detection is
+    // skipped for the matching benchmark.
+    //
+    // "regressions_first_commits": {
+    //    "some_benchmark": "352cdf",  // Consider regressions only after this commit
+    //    "another_benchmark": null,   // Skip regression detection altogether
+    // },
+
+    // The thresholds for relative change in results, after which `asv
+    // publish` starts reporting regressions. Dictionary of the same
+    // form as in ``regressions_first_commits``, with values
+    // indicating the thresholds.  If multiple entries match, the
+    // maximum is taken. If no entry matches, the default is 5%.
+    //
+    // "regressions_thresholds": {
+    //    "some_benchmark": 0.01,     // Threshold of 1%
+    //    "another_benchmark": 0.5,   // Threshold of 50%
+    // },
+}

benchmarks/__init__.py

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+

benchmarks/benchmarks.py

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+# Write the benchmarking functions here.
+# See "Writing benchmarks" in the asv docs for more information.
+
+
+class TimeSuite:
+    """
+    An example benchmark that times the performance of various kinds
+    of iterating over dictionaries in Python.
+    """
+    def setup(self):
+        self.d = {}
+        for x in range(500):
+            self.d[x] = None
+
+    def time_keys(self):
+        for key in self.d.keys():
+            pass
+
+    def time_values(self):
+        for value in self.d.values():
+            pass
+
+    def time_range(self):
+        d = self.d
+        for key in range(500):
+            x = d[key]
+
+
+class MemSuite:
+    def mem_list(self):
+        return [0] * 256

machine_learning/benchmarks/qk_base_benchmark.py

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+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2022.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Base class for QuantumKernel and QuantumKernelTraining based classifier benchmarks."""
+from abc import ABC
+from typing import Optional
+
+from qiskit.algorithms.optimizers import Optimizer
+from qiskit.circuit.library import ZZFeatureMap
+from qiskit_machine_learning.kernels import QuantumKernel
+from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer
+from qiskit import QuantumCircuit
+from qiskit.circuit import ParameterVector
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
+
+import numpy as np
+
+from .base_classifier_benchmark import BaseClassifierBenchmark
+
+
+class QKernelBaseClassifierBenchmark(BaseClassifierBenchmark, ABC):
+    """Base class for quantum kernel benchmarks."""
+
+    def __init__(self) -> None:
+        reshaper = FunctionTransformer(lambda x: x.reshape(-1, 1))
+        encoder = OneHotEncoder(sparse=False)
+        super().__init__(
+            synthetic_label_encoder=Pipeline([("reshape", reshaper), ("one hot", encoder)]),
+            iris_num_classes=2,
+            iris_label_encoder=Pipeline([("reshape", reshaper), ("one hot", encoder)]),
+        )
+
+    def _construct_quantumkernel_classical_classifier(
+        self,
+        quantum_instance_name: str,
+        method="quantumclassical",
+        num_qubits=1,
+    ) -> QuantumKernel:
+        """This method just create the matrix from the quantum kernel"""
+        kernelmatrix = self._construct_quantumkernel(num_qubits, quantum_instance_name, method)
+        return kernelmatrix
+
+    def _construct_quantumkerneltrainer(
+        self,
+        quantum_instance_name: str,
+        optimizer: Optional[Optimizer] = None,
+        loss_function: str = None,
+        method="quantum",
+        num_qubits=1,
+    ) -> QuantumKernelTrainer:
+        """This method returns the quantumkerneltrainer"""
+        kernel = self._construct_quantumkernel(num_qubits, quantum_instance_name, method)
+        qkt = QuantumKernelTrainer(
+            quantum_kernel=kernel,
+            loss=loss_function,
+            optimizer=optimizer,
+            initial_point=[np.pi / 2],
+        )
+        return qkt
+
+    def _construct_quantumkernel(
+        self,
+        num_inputs: int,
+        quantum_instance_name: str,
+        method: str,
+    ) -> QuantumKernel:
+        """Construct a QuantumKernel"""
+        if method == "quantumclassical":
+            feature_map = ZZFeatureMap(num_inputs, reps=2, entanglement="linear")
+            qkernel = QuantumKernel(
+                feature_map=feature_map, quantum_instance=self.backends[quantum_instance_name]
+            )
+            return qkernel
+        elif method == "quantum":
+            user_params = ParameterVector("θ", 1)
+            fm0 = QuantumCircuit(num_inputs)
+            for i in range(num_inputs):
+                fm0.ry(user_params[0], i)
+            fm1 = ZZFeatureMap(num_inputs, reps=2, entanglement="linear")
+            feature_map = fm0.compose(fm1)
+            qkernel = QuantumKernel(
+                feature_map=feature_map,
+                user_parameters=user_params,
+                quantum_instance=self.backends[quantum_instance_name],
+            )
+            return qkernel
+        else:
+            return ValueError(f"Unsupported method: {method}")