Prep-ing for 0.8 (#53)

* Migrating `qiskit_algorithms` (qiskit-community#817)

* Update README.md

* Generalize the Einstein summation signature

* Add reno

* Update Copyright

* Rename and add test

* Update Copyright

* Add docstring for `test_get_einsum_signature`

* Correct spelling

* Disable spellcheck for comments

* Add `docstring` in pylint dict

* Delete example in docstring

* Add Einstein in pylint dict

* Add full use case in einsum dict

* Spelling and type ignore

* Spelling and type ignore

* Spelling and type ignore

* Spelling and type ignore

* Spelling and type ignore

* Remove for loop in einsum function and remove Literal arguments (1/2)

* Remove for loop in einsum function and remove Literal arguments (1/2)

* Remove for loop in einsum function and remove Literal arguments (2/2)

* Update RuntimeError msg

* Update RuntimeError msg - line too long

* Trigger CI

* Merge algos, globals.random to fix

* Fixed `algorithms_globals`

* Import /tests and run CI locally

* Fix copyrights and some spellings

* Ignore mypy in 8 instances

* Merge spell dicts

* Black reformatting

* Black reformatting

* Add reno

* Lint sanitize

* Pylint

* Pylint

* Pylint

* Pylint

* Fix relative imports in tutorials

* Fix relative imports in tutorials

* Remove algorithms from Jupyter magic methods

* Temporarily disable "Run stable tutorials" tests

* Change the docstrings with imports from qiskit_algorithms

* Styling

* Update qiskit_machine_learning/optimizers/gradient_descent.py

Co-authored-by: Declan Millar <[email protected]>

* Update qiskit_machine_learning/optimizers/optimizer_utils/learning_rate.py

Co-authored-by: Declan Millar <[email protected]>

* Add more tests for utils

* Add more tests for optimizers: adam, bobyqa, gsls and imfil

* Fix random seed for volatile optimizers

* Fix random seed for volatile optimizers

* Add more tests

* Pylint dict

* Activate scikit-quant-0.8.2

* Remove scikit-quant methods

* Remove scikit-quant methods (2)

* Edit the release notes and Qiskit version 1+

* Edit the release notes and Qiskit version 1+

* Add Qiskit 1.0 upgrade in reno

* Add Qiskit 1.0 upgrade in reno

* Add Qiskit 1.0 upgrade in reno

* Apply line breaks

* Restructure line breaks

---------

Co-authored-by: FrancescaSchiav <[email protected]>
Co-authored-by: M. Emre Sahin <[email protected]>
Co-authored-by: Declan Millar <[email protected]>

* Revamp readme pt2 (qiskit-community#822)

* Restructure README.md
---------

Co-authored-by: Steve Wood <[email protected]>

* Fix lint errors due to Pylint 3.3.0 update in CI (qiskit-community#833)

* disable=too-many-positional-arguments

* Transfer pylint rc to toml

* Transfer pylint rc to toml

* Remove Python 3.8 from CI (qiskit-community#824) (qiskit-community#826)

* Remove Python 3.8 in CI (qiskit-community#824)

* Correct `tmp` dirs (qiskit-community#818)

* Correct unit py version (qiskit-community#818)

* Add reno (qiskit-community#818)

* Finalze removal of py38 (qiskit-community#818)

* Spelling

* Remove duplicate tmp folder

* Updated the release note

* Bump min pyversion in toml

* Remove ipython constraints

* Update reno

* Reestablish latest Pytorch and Numpy (qiskit-community#818) (qiskit-community#827)

* Reestablish latest Pytorch and Numpy (qiskit-community#818)

* Keep pinned Numpy

* Keep pinned Numpy

* Fix numpy min version

* Fix RawFeatureVector for failing test case (qiskit-community#838)

* Alter RawFeatureVector normalization

* Alter RawFeatureVector normalization

* Release nbconvert constraints (qiskit-community#842)

* Remove redundant MacOS 14 image in CI (qiskit-community#841)

* Remove redundant MacOS 14 image

* Update with macos-latest-large

* Revert "Update with macos-latest-large"

This reverts commit 14f945e.

* Update with macos-latest-large

* Update v2 (#54)

* bugfix for V1

* formatting

---------

Co-authored-by: oscar-wallis <[email protected]>

---------

Co-authored-by: Edoardo Altamura <[email protected]>
Co-authored-by: FrancescaSchiav <[email protected]>
Co-authored-by: M. Emre Sahin <[email protected]>
Co-authored-by: Declan Millar <[email protected]>
Co-authored-by: Steve Wood <[email protected]>

Author: 6 people

constraints.txt

@@ -1 +1 @@
-numpy>=1.20 
+numpy>=1.20

qiskit_machine_learning/circuit/library/raw_feature_vector.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2024.
 #
 # 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
@@ -176,7 +176,8 @@ def _define(self):
                 raise QiskitError("Cannot define a ParameterizedInitialize with unbound parameters")
 
         # normalize
-        normalized = np.array(cleaned_params) / np.linalg.norm(cleaned_params)
+        norm = np.linalg.norm(cleaned_params)
+        normalized = cleaned_params if np.isclose(norm, 1) else cleaned_params / norm
 
         circuit = QuantumCircuit(self.num_qubits)
         circuit.initialize(normalized, range(self.num_qubits))

qiskit_machine_learning/gradients/base/base_sampler_gradient.py

@@ -61,6 +61,7 @@ def __init__(
         self._default_options = Options()
         self._pass_manager = pass_manager
         self._len_quasi_dist = len_quasi_dist
+
         if options is not None:
             self._default_options.update_options(**options)
         self._gradient_circuit_cache: dict[tuple, GradientCircuit] = {}

qiskit_machine_learning/neural_networks/estimator_qnn.py

@@ -31,6 +31,7 @@
     EstimatorGradientResult,
     ParamShiftEstimatorGradient,
 )
+
 from ..circuit.library import QNNCircuit
 from ..exceptions import QiskitMachineLearningError
 
@@ -323,9 +324,11 @@ def _backward(
             job = None
 
             if self._input_gradients:
+
                 job = self.gradient.run(
                     circuits, observables, param_values
                 )  # type: ignore[arg-type]
+
             elif len(parameter_values[0]) > self._num_inputs:
                 params = [self._circuit.parameters[self._num_inputs :]] * num_circuits
                 job = self.gradient.run(

qiskit_machine_learning/neural_networks/sampler_qnn.py

@@ -37,6 +37,7 @@
 
 from .neural_network import NeuralNetwork
 
+
 if _optionals.HAS_SPARSE:
     # pylint: disable=import-error
     from sparse import SparseArray
@@ -349,7 +350,6 @@ def _postprocess_gradient(
             )
             weights_grad = DOK((num_samples, *self._output_shape, self._num_weights))
         else:
-
             input_grad = (
                 np.zeros((num_samples, *self._output_shape, self._num_inputs))
                 if self._input_gradients
@@ -441,10 +441,12 @@ def _backward(
             circuits = [self._circuit] * num_samples
             job = None
             if self._input_gradients:
-                job = self.gradient.run(circuits, parameter_values)
+                job = self.gradient.run(circuits, parameter_values)  # type: ignore[arg-type]
             elif len(parameter_values[0]) > self._num_inputs:
                 params = [self._circuit.parameters[self._num_inputs :]] * num_samples
-                job = self.gradient.run(circuits, parameter_values, parameters=params)
+                job = self.gradient.run(
+                    circuits, parameter_values, parameters=params  # type: ignore[arg-type]
+                )
 
             if job is not None:
                 try:

qiskit_machine_learning/state_fidelities/compute_uncompute.py

@@ -18,7 +18,6 @@
 from copy import copy
 
 from qiskit import QuantumCircuit
-
 from qiskit.primitives import BaseSampler, BaseSamplerV1, SamplerResult, StatevectorSampler
 from qiskit.primitives.base import BaseSamplerV2
 
@@ -29,6 +28,7 @@
 from qiskit.providers import Options
 
 from ..exceptions import AlgorithmError, QiskitMachineLearningError
+
 from .base_state_fidelity import BaseStateFidelity
 from .state_fidelity_result import StateFidelityResult
 from ..algorithm_job import AlgorithmJob
@@ -180,6 +180,7 @@ def _run(
         # primitive's default options.
         opts = copy(self._default_options)
         opts.update_options(**options)
+
         if isinstance(self._sampler, BaseSamplerV1):
             sampler_job = self._sampler.run(
                 circuits=circuits, parameter_values=values, **opts.__dict__