Update intersphinx mapping and other URLs pointing to IQP Classic (#933) (#940)

* Update intersphinx mapping and others URLs pointing to IQP Classic

* fix copyright date

* fix broken links

(cherry picked from commit 3e7c48b)

Co-authored-by: Arnau Casau <[email protected]>

Author: mergify[bot]

README.md

@@ -46,9 +46,9 @@ to solve classification or regression problems respectively. It is also compatib
 
 Qiskit Machine Learning defines a generic interface for neural networks, implemented by two core (derived) primitives:
 
-- **[`EstimatorQNN`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html):** Leverages the [`Estimator`](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseEstimator) primitive, combining parametrized quantum circuits with quantum mechanical observables. The output is the expected value of the observable.
+- **[`EstimatorQNN`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html):** Leverages the [`Estimator`](https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseEstimator) primitive, combining parametrized quantum circuits with quantum mechanical observables. The output is the expected value of the observable.
 
-- **[`SamplerQNN`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html):** Leverages the [`Sampler`](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler) primitive, translating bit-string counts into the desired outputs.
+- **[`SamplerQNN`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html):** Leverages the [`Sampler`](https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler) primitive, translating bit-string counts into the desired outputs.
 
 To train and use neural networks, Qiskit Machine Learning provides learning algorithms such as the [`NeuralNetworkClassifier`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkClassifier.html#qiskit_machine_learning.algorithms.NeuralNetworkClassifier) 
 and [`NeuralNetworkRegressor`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkRegressor.html#qiskit_machine_learning.algorithms.NeuralNetworkRegressor). 
@@ -102,7 +102,7 @@ For more details on how to do so and much more, follow the instructions in the
 > Learning, Qiskit `1.0` or above will be required. If you have a pre-`1.0` version of Qiskit 
 > installed in your environment (however it was installed), you should upgrade to `1.x` to 
 > continue using the latest features. You may refer to the 
-> official [Qiskit 1.0 Migration Guide](https://docs.quantum.ibm.com/api/migration-guides/qiskit-1.0) 
+> official [Qiskit 1.0 Migration Guide](https://quantum.cloud.ibm.com/docs/migration-guides/qiskit-1.0) 
 > for detailed instructions and examples on how to upgrade Qiskit.
 
 ----------------------------------------------------------------------------------------------------

SECURITY.md

@@ -8,7 +8,7 @@ Qiskit Machine Learning supports one minor version release at a time, both for b
 > For example, if the most recent release is `0.7.2`, then the current major release series is `0.x` the current minor 
 > release is `0.7.x`, with `0.7.2` being the current patch release.
 
-As an additional resource, you can find more details on the release and support schedule of Qiskit in the [documentation](https://docs.quantum.ibm.com/start/install#release-schedule).
+As an additional resource, you can find more details on the release and support schedule of Qiskit in the [documentation](https://quantum.cloud.ibm.com/docs/en/open-source/qiskit-sdk-version-strategy).
 
 ## Reporting a Vulnerability
 

docs/conf.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 2025.
 #
 # 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 +176,7 @@
     "numpy": ("https://numpy.org/doc/stable", None),
     "scipy": ("https://docs.scipy.org/doc/scipy", None),
     "sklearn": ("https://scikit-learn.org/stable", None),
-    "qiskit": ("https://docs.quantum.ibm.com/api/qiskit", None),
+    "qiskit": ("https://quantum.cloud.ibm.com/docs/api/qiskit", None),
 }
 
 html_context = {"analytics_enabled": True}

docs/getting_started.rst

@@ -8,7 +8,7 @@ Installation
 ============
 
 Qiskit Machine Learning depends on Qiskit, which has its own
-`installation instructions <https://docs.quantum.ibm.com/start/install>`__ detailing
+`installation instructions <https://quantum.cloud.ibm.com/docs/guides/install-qiskit>`__ detailing
 installation options and its supported environments/platforms. You should refer to
 that first. Then the information here can be followed which focuses on the additional installation
 specific to Qiskit Machine Learning.
@@ -21,7 +21,7 @@ See :ref:`optional_installs` for more information.
 
     .. tab-item:: Start locally
 
-        The simplest way to get started is to follow the installation guide for Qiskit `here <https://docs.quantum.ibm.com/start/install>`__
+        The simplest way to get started is to follow the installation guide for Qiskit `here <https://quantum.cloud.ibm.com/docs/guides/install-qiskit>`__
 
         In your virtual environment, where you installed Qiskit, install ``qiskit-machine-learning`` as follows:
 
@@ -44,7 +44,7 @@ See :ref:`optional_installs` for more information.
 
        Since Qiskit Machine Learning depends on Qiskit, and its latest changes may require new or changed
        features of Qiskit, you should first follow Qiskit's `"Install from source"` instructions
-       `here <https://docs.quantum.ibm.com/start/install-qiskit-source>`__
+       `here <https://quantum.cloud.ibm.com/docs/guides/install-qiskit-source>`__
 
        .. raw:: html
 
@@ -114,7 +114,7 @@ Migration to Qiskit 1.x
    Learning, Qiskit ``1.0`` or above will be required. If you have a pre-``1.0`` version of Qiskit
    installed in your environment (however it was installed), you should upgrade to ``1.x`` to
    continue using the latest features. You may refer to the
-   official `Qiskit 1.0 Migration Guide <https://docs.quantum.ibm.com/api/migration-guides/qiskit-1.0>`_
+   official `Qiskit 1.0 Migration Guide <https://quantum.cloud.ibm.com/docs/migration-guides/qiskit-1.0>`_
    for detailed instructions and examples on how to upgrade Qiskit.
 
 

docs/index.rst

@@ -41,11 +41,11 @@ Quantum Neural Networks (QNNs)
 Qiskit Machine Learning defines a generic interface for neural networks, implemented by two core (derived) primitives:
 
 - :class:`~qiskit_machine_learning.neural_networks.EstimatorQNN` leverages the Qiskit
-  `Estimator <https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseEstimator>`__ primitive, combining parametrized quantum circuits
+  `Estimator <https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseEstimator>`__ primitive, combining parametrized quantum circuits
   with quantum mechanical observables. The output is the expected value of the observable.
 
 - :class:`~qiskit_machine_learning.neural_networks.SamplerQNN` leverages the Qiskit
-  `Sampler <https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler>`__ primitive,
+  `Sampler <https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler>`__ primitive,
   translating bit-string counts into the desired outputs.
 
 To train and use neural networks, Qiskit Machine Learning provides learning algorithms such as the :class:`~qiskit_machine_learning.algorithms.NeuralNetworkClassifier`

docs/migration/01_migration_guide_0.5.rst

@@ -35,36 +35,36 @@ respectively, that implement these two operations:
 
 -  Sampler class calculates probabilities or quasi-probabilities of
    bitstrings from quantum circuits. The base class is
-   `qiskit.primitives.BaseSampler <https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler>`__.
+   `qiskit.primitives.BaseSampler <https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseSampler>`__.
 -  Estimator class estimates expectation values of quantum circuits and
    observables. The base class is
-   `qiskit.primitives.BaseEstimator <https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseEstimator>`__.
+   `qiskit.primitives.BaseEstimator <https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.BaseEstimator>`__.
 
 Qiskit Terra provides core interfaces and two implementations:
 
 -  The reference implementation that is statevector based. This
    implementation does require a backend or a simulator, it relies on
    the classes from the
-   `quantum_info <https://docs.quantum.ibm.com/api/qiskit/quantum_info>`__
+   `quantum_info <https://quantum.cloud.ibm.com/docs/api/qiskit/quantum_info>`__
    package.
 -  The backend based primitives are to support provider/backends that do
    not support primitives directly. This implementation requires an
    instance of a backend to be passed to a primitive.
 
 More information on the Qiskit Terra primitives can be found in the
-`documentation <https://docs.quantum.ibm.com/api/qiskit/primitives>`__.
+`documentation <https://quantum.cloud.ibm.com/docs/api/qiskit/primitives>`__.
 
 It is worth mentioning other implementations as well:
 
 -  Aer primitives should be used for Aer simulator. They extend
    corresponding interfaces from Terra and can be used in the same way
    as primitives from Terra. See
-   `documentation <https://docs.quantum.ibm.com/api/qiskit/0.39/aer_primitives>`__
+   `documentation <https://quantum.cloud.ibm.com/docs/api/qiskit/0.39/aer_primitives>`__
    for more information.
 -  The runtime primitives to be used with IBM devices. This is an
    implementation that is focused on cloud computing on actual hardware.
    See
-   `here <https://docs.quantum.ibm.com/api/qiskit-ibm-runtime>`__.
+   `here <https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime>`__.
 
 Along with the primitives Terra has some primitive-like algorithms that
 are highly useful in QML and used by the new 0.5 functions:
@@ -240,7 +240,7 @@ none is passed. But here, we create it manually for illustrative
 purposes. To create a fidelity instance we pass a sampler. The sampler
 is the reference implementation and defines where our quantum circuits
 are executed. You may create a sampler instance from
-`QiskitRuntimeService <https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.QiskitRuntimeService>`__
+`QiskitRuntimeService <https://quantum.cloud.ibm.com/docs/en/api/qiskit-ibm-runtime/qiskit-runtime-service>`__
 to leverage Qiskit runtime services.
 
 .. code:: ipython3
@@ -596,7 +596,7 @@ Building a regressor using ``EstimatorQNN``
 
 Create an instance of the reference Estimator. You may create an
 estimator instance from
-`QiskitRuntimeService <https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.QiskitRuntimeService>`__
+`QiskitRuntimeService <https://quantum.cloud.ibm.com/docs/en/api/qiskit-ibm-runtime/qiskit-runtime-service>`__
 to leverage Qiskit runtime services.
 
 .. code:: ipython3
@@ -679,7 +679,7 @@ deprecated or pending deprecation:
    Qiskit Programs that embed Qiskit Runtime in the algorithmic
    interfaces and facilitate usage of algorithms and scripts in the
    cloud. You should use
-   `QiskitRuntimeService <https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.QiskitRuntimeService>`__
+   `QiskitRuntimeService <https://quantum.cloud.ibm.com/docs/en/api/qiskit-ibm-runtime/qiskit-runtime-service>`__
    to leverage primitives and runtimes.
 
 .. code:: ipython3

docs/migration/02_migration_guide_0.8.rst

@@ -23,7 +23,7 @@ Overview of the primitives
 
 With the launch of `Qiskit 1.0`, V1 primitives are deprecated and replaced by V2 primitives. Further details
 are available in the 
-`V2 primitives migration guide <https://docs.quantum.ibm.com/migration-guides/v2-primitives>`__.
+`V2 primitives migration guide <https://quantum.cloud.ibm.com/docs/migration-guides/v2-primitives>`__.
 
 The Qiskit Machine Learning 0.8 update aligns with the Qiskit IBM Runtime’s Primitive Unified Block (PUB) 
 requirements and the constraints of the instruction set architecture (ISA) for circuits and observables. 

docs/tutorials/01_neural_networks.ipynb

@@ -64,10 +64,10 @@
     "3. [SamplerQNN](https://qiskit-community.github.io/qiskit-machine-learning/locale/fr_FR/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html): A network based on the samples resulting from measuring a quantum circuit.\n",
     "\n",
     "\n",
-    "These implementations are based on the [qiskit primitives](https://docs.quantum.ibm.com/api/qiskit/primitives). The primitives are the entry point to run QNNs on either a simulator or real quantum hardware. Each implementation, `EstimatorQNN` and `SamplerQNN`, takes in an optional instance of its corresponding primitive, which can be any subclass of `BaseEstimator` and `BaseSampler`, respectively.\n",
+    "These implementations are based on the [qiskit primitives](https://quantum.cloud.ibm.com/docs/api/qiskit/primitives). The primitives are the entry point to run QNNs on either a simulator or real quantum hardware. Each implementation, `EstimatorQNN` and `SamplerQNN`, takes in an optional instance of its corresponding primitive, which can be any subclass of `BaseEstimator` and `BaseSampler`, respectively.\n",
     "\n",
     "The `qiskit.primitives` module provides a reference implementation for the `Sampler` and `Estimator` classes to run statevector simulations. By default, if no instance is passed to a QNN class, an instance of the corresponding reference primitive (`Sampler` or `Estimator`) is created automatically by the network.\n",
-    "For more information about primitives please refer to the [primitives documentation](https://docs.quantum.ibm.com/api/qiskit/primitives).\n",
+    "For more information about primitives please refer to the [primitives documentation](https://quantum.cloud.ibm.com/docs/api/qiskit/primitives).\n",
     "\n",
     "The `NeuralNetwork` class is the interface for all QNNs available in `qiskit-machine-learning`.\n",
     "It exposes a forward and a backward pass that take data samples and trainable weights as input.\n",

docs/tutorials/02a_training_a_quantum_model_on_a_real_dataset.ipynb

@@ -566,7 +566,7 @@
     "\n",
     "Now let's see what we can tune to get even better models.\n",
     "\n",
-    "- The key components are the feature map and the ansatz. You can tweak parameters. In our case, you may change the `reps` parameter that specifies how repetitions of a gate pattern we add to the circuit. Larger values lead to more entanglement operations and more parameters. Thus, the model can be more flexible, but the higher number of parameters also adds complexity, and training such a model usually takes more time. Furthermore, we may end up overfitting the model. You can try the other feature maps and ansatzes available in the [Qiskit circuit library](https://docs.quantum.ibm.com/api/qiskit/circuit_library#n-local-circuits), or you can come up with custom circuits.\n",
+    "- The key components are the feature map and the ansatz. You can tweak parameters. In our case, you may change the `reps` parameter that specifies how repetitions of a gate pattern we add to the circuit. Larger values lead to more entanglement operations and more parameters. Thus, the model can be more flexible, but the higher number of parameters also adds complexity, and training such a model usually takes more time. Furthermore, we may end up overfitting the model. You can try the other feature maps and ansatzes available in the [Qiskit circuit library](https://quantum.cloud.ibm.com/docs/api/qiskit/circuit_library#n-local-circuits), or you can come up with custom circuits.\n",
     "- You may try other optimizers. Qiskit contains a bunch of them. Some of them are gradient-free, others not. If you choose a gradient-based optimizer, e.g., `L_BFGS_B`, expect the training time to increase. Additionally to the objective function, these optimizers must evaluate the gradient with respect to the training parameters, which leads to an increased number of circuit executions per iteration.\n",
     "- Another option is to randomly (or deterministically) sample `initial_point` and fit the model several times.\n",
     "\n",

docs/tutorials/03_quantum_kernel.ipynb

@@ -231,9 +231,9 @@
     "\n",
     "We use the [FidelityQuantumKernel](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.kernels.FidelityQuantumKernel.html) class, and pass two input arguments to its constructor: \n",
     "\n",
-    "1. `feature_map`: in this case, a two-qubit [ZZFeatureMap](https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.library.ZZFeatureMap).\n",
+    "1. `feature_map`: in this case, a two-qubit [ZZFeatureMap](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.ZZFeatureMap).\n",
     "\n",
-    "2. `fidelity`: in this case, the [ComputeUncompute](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.state_fidelities.ComputeUncompute.html) fidelity subroutine that leverages the [Sampler](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.Sampler) primitive.\n",
+    "2. `fidelity`: in this case, the [ComputeUncompute](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.state_fidelities.ComputeUncompute.html) fidelity subroutine that leverages the [Sampler](https://quantum.cloud.ibm.com/docs/api/qiskit/1.4/qiskit.primitives.Sampler) primitive.\n",
     "\n",
     "**NOTE:** If you don't pass a `Sampler` or `Fidelity` instance, then the instances of the reference `Sampler` and `ComputeUncompute` classes (found in `qiskit.primitives`) will be created by default."
    ]