Remove references from docs

docs/apidoc/index.rst

@@ -57,5 +57,4 @@ Deprecated Modules
 .. toctree::
    :maxdepth: 1
 
-   algorithms
    opflow

docs/migration_guides/algorithms_migration.rst

@@ -5,7 +5,7 @@ Algorithms Migration Guide
 TL;DR
 =====
 
-The :mod:`qiskit.algorithms` module has been fully refactored to use the :mod:`~qiskit.primitives`, for circuit execution, instead of the :class:`~qiskit.utils.QuantumInstance`, which is now deprecated.
+The ``qiskit.algorithms`` module has been fully refactored to use the :mod:`~qiskit.primitives`, for circuit execution, instead of the :class:`~qiskit.utils.QuantumInstance`, which is now deprecated.
 
 There have been **3 types of refactoring**:
 
@@ -15,7 +15,7 @@ There have been **3 types of refactoring**:
     .. attention::
 
        **Careful with import paths!!** The legacy algorithms are still importable directly from
-       :mod:`qiskit.algorithms`. Until the legacy imports are removed, this convenience import is not available
+       ``qiskit.algorithms``. Until the legacy imports are removed, this convenience import is not available
        for the refactored algorithms. Thus, to import the refactored algorithms you must always
        **specify the full import path** (e.g., ``from qiskit.algorithms.eigensolvers import VQD``)
 
@@ -31,7 +31,7 @@ There have been **3 types of refactoring**:
     - `Phase Estimators`_
 
 
-3. Algorithms that were deprecated and are now removed entirely from :mod:`qiskit.algorithms`. These are algorithms that do not currently serve
+3. Algorithms that were deprecated and are now removed entirely from ``qiskit.algorithms``. These are algorithms that do not currently serve
    as building blocks for applications. Their main value is educational, and as such, will be kept as tutorials
    in the qiskit textbook. You can consult the tutorials in the following links:
 
@@ -40,18 +40,18 @@ There have been **3 types of refactoring**:
 
 
 The remainder of this migration guide will focus on the algorithms with migration alternatives within
-:mod:`qiskit.algorithms`, that is, those under refactoring types 1 and 2.
+``qiskit.algorithms``, that is, those under refactoring types 1 and 2.
 
 Background
 ==========
 
 *Back to* `TL;DR`_
 
-The :mod:`qiskit.algorithms` module was originally built on top of the :mod:`qiskit.opflow` library and the
+The ``qiskit.algorithms`` module was originally built on top of the :mod:`qiskit.opflow` library and the
 :class:`~qiskit.utils.QuantumInstance` utility. The development of the :mod:`~qiskit.primitives`
 introduced a higher-level execution paradigm, with the ``Estimator`` for computation of
 expectation values for observables, and ``Sampler`` for executing circuits and returning probability
-distributions. These tools allowed to refactor the :mod:`qiskit.algorithms` module, and deprecate both
+distributions. These tools allowed to refactor the ``qiskit.algorithms`` module, and deprecate both
 :mod:`qiskit.opflow` and :class:`~qiskit.utils.QuantumInstance`.
 
 .. attention::
@@ -75,7 +75,7 @@ How to choose a primitive configuration for your algorithm
 *Back to* `TL;DR`_
 
 The classes in
-:mod:`qiskit.algorithms` are initialized with any implementation of :class:`qiskit.primitive.BaseSampler` or class:`qiskit.primitive.BaseEstimator`.
+``qiskit.algorithms`` are initialized with any implementation of :class:`qiskit.primitive.BaseSampler` or class:`qiskit.primitive.BaseEstimator`.
 
 Once the kind of primitive is known, you can choose between the primitive implementations that better adjust to your case. For example:
 

docs/migration_guides/opflow_migration.rst

@@ -458,7 +458,7 @@ are used "under the hood" in the original code:
      - :class:`~qiskit.quantum_info.Operator`
 
    * - :class:`~qiskit.opflow.primitive_ops.PauliOp`
-     - :class:`~qiskit.quantum_info.Pauli`. For direct compatibility with classes in :mod:`qiskit.algorithms`,
+     - :class:`~qiskit.quantum_info.Pauli`. For direct compatibility with classes in ``qiskit.algorithms``,
        wrap in :class:`~qiskit.quantum_info.SparsePauliOp`.
 
    * - :class:`~qiskit.opflow.primitive_ops.PauliSumOp`
@@ -1410,7 +1410,7 @@ Gradients
 
 The opflow :mod:`~qiskit.opflow.gradients` framework has been replaced by the new :mod:`qiskit.algorithms.gradients`
 module. The new gradients are **primitive-based subroutines** commonly used by algorithms and applications, which
-can also be executed in a standalone manner. For this reason, they now reside under :mod:`qiskit.algorithms`.
+can also be executed in a standalone manner. For this reason, they now reside under ``qiskit.algorithms``.
 
 The former gradient framework contained base classes, converters and derivatives. The "derivatives"
 followed a factory design pattern, where different methods could be provided via string identifiers

qiskit/primitives/backend_estimator.py

@@ -98,8 +98,7 @@ class BackendEstimator(BaseEstimator[PrimitiveJob[EstimatorResult]]):
     (or :class:`~.BackendV1`) object in the :class:`~.BaseEstimator` API. It
     facilitates using backends that do not provide a native
     :class:`~.BaseEstimator` implementation in places that work with
-    :class:`~.BaseEstimator`, such as algorithms in :mod:`qiskit.algorithms`
-    including :class:`~.qiskit.algorithms.minimum_eigensolvers.VQE`. However,
+    :class:`~.BaseEstimator`. However,
     if you're using a provider that has a native implementation of
     :class:`~.BaseEstimator`, it is a better choice to leverage that native
     implementation as it will likely include additional optimizations and be

qiskit/primitives/backend_sampler.py

@@ -38,8 +38,7 @@ class BackendSampler(BaseSampler[PrimitiveJob[SamplerResult]]):
     any measurement mitigation, it just computes the probability distribution
     from the counts. It facilitates using backends that do not provide a
     native :class:`~.BaseSampler` implementation in places that work with
-    :class:`~.BaseSampler`, such as algorithms in :mod:`qiskit.algorithms`
-    including :class:`~.qiskit.algorithms.minimum_eigensolvers.SamplingVQE`.
+    :class:`~.BaseSampler`.
     However, if you're using a provider that has a native implementation of
     :class:`~.BaseSampler`, it is a better choice to leverage that native
     implementation as it will likely include additional optimizations and be

qiskit/providers/__init__.py

@@ -144,8 +144,8 @@
 backend. It also provides the :meth:`~qiskit.providers.BackendV2.run` method which can
 run the :class:`~qiskit.circuit.QuantumCircuit` objects and/or
 :class:`~qiskit.pulse.Schedule` objects. This enables users and other Qiskit
-APIs, such as :func:`~qiskit.execute_function.execute` and higher level algorithms in
-:mod:`qiskit.algorithms`, to get results from executing circuits on devices in a standard
+APIs, such as :func:`~qiskit.execute_function.execute` to get results from
+executing circuits on devices in a standard
 fashion regardless of how the backend is implemented. At a high level the basic
 steps for writing a provider are:
 
@@ -635,7 +635,7 @@ def status(self):
 provider-specific :class:`~.Sampler` implementation that leverages the
 ``M3Mitigation`` class internally to run the circuits and return
 quasi-probabilities directly from mthree in the result. Doing this would
-enable algorithms from :mod:`qiskit.algorithms` to get the best results with
+enable algorithms to get the best results with
 mitigation applied directly from your backends. You can refer to the
 documentation in :mod:`qiskit.primitives` on how to write custom
 implementations. Also the built-in implementations: :class:`~.Sampler`,

qiskit/transpiler/synthesis/aqc/aqc.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2022.
+# (C) Copyright IBM 2022, 2023.
 #
 # 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

qiskit/transpiler/synthesis/aqc/aqc_plugin.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # 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

qiskit/utils/__init__.py

@@ -48,9 +48,8 @@
 
 A QuantumInstance holds the Qiskit `backend` as well as a number of compile and
 runtime parameters controlling circuit compilation and execution. Quantum
-:mod:`algorithms <qiskit.algorithms>`
-are run on a device or simulator by passing a QuantumInstance setup with the desired
-backend etc.
+algorithms are run on a device or simulator by passing a QuantumInstance setup
+with the desired backend etc.
 
 
 Optional Dependency Checkers (:mod:`qiskit.utils.optionals`)

qiskit/utils/mitigation/__init__.py

@@ -44,7 +44,7 @@
 From these calibrations, it is possible to correct the average results
 of another experiment of interest. These tools are intended for use solely
 with the :class:`~qiskit.utils.QuantumInstance` class as part of
-:mod:`qiskit.algorithms` and :mod:`qiskit.opflow`.
+:mod:`qiskit.opflow`.
 
 .. autosummary::
    :toctree: ../stubs/

releasenotes/notes/0.17/prepare-0.17-2ab9429b69e1d25c.yaml

@@ -2,11 +2,11 @@
 prelude: |
     The Qiskit Terra 0.17.0 includes many new features and bug fixes. The major
     new feature for this release is the introduction of the
-    :mod:`qiskit.algorithms` and :mod:`qiskit.opflow` modules which were
+    ``qiskit.algorithms`` and :mod:`qiskit.opflow` modules which were
     migrated and adapted from the :mod:`qiskit.aqua` project.
 features:
   - |
-    A new module :mod:`qiskit.algorithms` has been introduced. This module
+    A new module ``qiskit.algorithms`` has been introduced. This module
     contains functionality equivalent to what has previously been
     provided by the :mod:`qiskit.aqua.algorithms` module (which is now
     deprecated) and provides the building blocks for constructing quantum

releasenotes/notes/0.18/requirements-updates-6059950dfde3cef6.yaml

@@ -6,7 +6,7 @@ upgrade:
     was done because of the wide use of the
     :class:`~qiskit.circuit.library.PhaseOracle` (which depends on
     having tweedledum installed) with several algorithms
-    from :mod:`qiskit.algorithms`.
+    from ``qiskit.algorithms``.
   - |
     The optional extra ``full-featured-simulators`` which could previously used
     to install ``qiskit-aer`` with something like

releasenotes/notes/0.19/readout-mitigation-classes-2ef175e232d791ae.yaml

@@ -8,7 +8,7 @@ features:
     on backends with measurement errors.
 
     Readout mitigator classes have two main methods:
-  
+
     * :meth:`~.BaseReadoutMitigator.expectation_value` which computes an
       mitigated expectation value and standard error of a diagonal operator from
       a noisy :class:`~qiskit.result.Counts` object.
@@ -17,7 +17,7 @@ features:
       mitigated :class:`~qiskit.result.QuasiDistribution`, including standard
       error, from a noisy counts object.
 
-    Note that currently the :mod:`qiskit.algorithms` module and the
+    Note that currently the ``qiskit.algorithms`` module and the
     :class:`~qiskit.utils.QuantumInstance` class still use the legacy mitigators
     migrated from Qiskit Ignis in :mod:`qiskit.utils.mitigation`. It is planned
     to upgrade the module to use the new mitigator classes and deprecate the legacy
@@ -26,12 +26,12 @@ features:
     Added the :class:`~qiskit.result.LocalReadoutMitigator` class for
     performing measurement readout error mitigation of local measurement
     errors. Local measuerment errors are those that are described by a
-    tensor-product of single-qubit measurement errors. 
-    
+    tensor-product of single-qubit measurement errors.
+
     This class can be initialized with a list of :math:`N` single-qubit of
     measurement error assignment matrices or from a backend using the readout
     error information in the backend properties.
-    
+
     Mitigation is implemented using local assignment-matrix inversion which has
     complexity of :math:`O(2^N)` for :math:`N`-qubit mitigation of
     :class:`~qiskit.result.QuasiDistribution` and expectation values.

releasenotes/notes/0.20/fix-algorithms-7f1b969e5b2447f9.yaml

@@ -13,7 +13,7 @@ upgrade:
 fixes:
   - |
     The :class:`~.AmplitudeAmplifier` is now correctly available from the root
-    :mod:`qiskit.algorithms` module directly. Previously it was not included
+    ``qiskit.algorithms`` module directly. Previously it was not included
     in the re-exported classes off the root module and was only accessible
     from ``qiskit.algorithms.amplitude_amplifiers``.
     Fixed `#7751 <https://github.com/Qiskit/qiskit-terra/pull/7752>`__.

releasenotes/notes/0.22/adapt-vqe-0f71234cb6ec92f8.yaml

@@ -1,7 +1,7 @@
 ---
 features:
   - |
-    Added a new algorithm class, :class:`~.AdaptVQE` to :mod:`qiskit.algorithms`
+    Added a new algorithm class, :class:`~.AdaptVQE` to ``qiskit.algorithms``
     This algorithm uses a :class:`qiskit.algorithms.minimum_eigensolvers.VQE`
     in combination with a pool of operators from which to build out an
     :class:`qiskit.circuit.library.EvolvedOperatorAnsatz` adaptively.

releasenotes/notes/0.22/prepare-0.22-118e15de86d36072.yaml

@@ -16,7 +16,7 @@ prelude: |
       Additionally, :class:`~.BackendV2` backends can now optionally set
       custom default plugins to use for the scheduling and translation stages.
 
-    * Updated algorithm implementations in :mod:`qiskit.algorithms` that leverage
+    * Updated algorithm implementations in ``qiskit.algorithms`` that leverage
       the :mod:`~.primitives` classes that implement the :class:`~.BaseSampler` and
       :class:`~.BaseEstimator`.
 

releasenotes/notes/0.22/project-dynamics-primitives-6003336d0866ca19.yaml

@@ -1,6 +1,6 @@
 features:
   - |
-    Added the :class:`~.PVQD` class to the time evolution framework in :mod:`qiskit.algorithms`.
+    Added the :class:`~.PVQD` class to the time evolution framework in ``qiskit.algorithms``.
     This class implements the projected Variational Quantum Dynamics (p-VQD) algorithm
     `Barison et al. <https://quantum-journal.org/papers/q-2021-07-28-512/>`_.
 

releasenotes/notes/0.24/remove-deprecated-factorizers-linear-solvers-4631870129749624.yaml

@@ -3,7 +3,7 @@ upgrade:
   - |
     The deprecated modules ``factorizers`` and ``linear_solvers``, containing
     ``HHL`` and ``Shor`` have been removed from
-    :mod:`qiskit.algorithms`. These functionalities
+    ``qiskit.algorithms``. These functionalities
     were originally deprecated as part of the 0.22.0 release (released on
     October 13, 2022). You can access the code through the Qiskit Textbook instead:
     `Linear Solvers (HHL) <https://learn.qiskit.org/course/ch-applications/solving-linear-systems-of-equations-using-hhl-and-its-qiskit-implementation>`_ ,

releasenotes/notes/0.25/deprecate-algorithms-7149dee2da586549.yaml

@@ -1,23 +1,23 @@
 ---
 deprecations:
   - |
-    The :mod:`qiskit.algorithms` module has been deprecated and will be removed
+    The ``qiskit.algorithms`` module has been deprecated and will be removed
     in a future release. It has been superseded by a new standalone library
     ``qiskit-algorithms`` which can be found on PyPi or on Github here:
 
     https://github.com/qiskit-community/qiskit-algorithms
 
-    The :mod:`qiskit.algorithms` will continue to work as before and bug fixes
+    The ``qiskit.algorithms`` module will continue to work as before and bug fixes
     will be made to module until its future removal, but active development
     of new features has moved to the new package.
-    If you're relying on :mod:`qiskit.algorithms` you should update your
+    If you're relying on ``qiskit.algorithms`` you should update your
     requirements to also include ``qiskit-algorithms`` and update the imports
     from ``qiskit.algorithms`` to ``qiskit_algorithms``. Please note that this
     new package does not include already deprecated algorithms code, including
     ``opflow`` and ``QuantumInstance``-based algorithms. If you have not yet
     migrated from ``QuantumInstance``-based to primitives-based algorithms,
     you should follow the migration guidelines in https://qisk.it/algo_migration.
-    The decision to migrate the :mod:`~.algorithms` module to a
+    The decision to migrate the ``qiskit.algorithms`` module to a
     separate package was made to clarify the purpose Qiskit and
     make a distinction between the tools and libraries built on top of it.
 

test/python/transpiler/aqc/test_aqc.py

@@ -15,10 +15,11 @@
 from functools import partial
 
 import unittest
+from test.python.transpiler.aqc.sample_data import ORIGINAL_CIRCUIT, INITIAL_THETAS
+
 import numpy as np
 from scipy.optimize import minimize
 
-from test.python.transpiler.aqc.sample_data import ORIGINAL_CIRCUIT, INITIAL_THETAS
 from qiskit.quantum_info import Operator
 from qiskit.test import QiskitTestCase
 from qiskit.transpiler.synthesis.aqc.aqc import AQC

test/python/transpiler/aqc/test_aqc_plugin.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2021, 2023.
 #
 # 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