Merge branch 'main' into pr/2088

.github/ISSUE_TEMPLATE/bug.yaml

@@ -1,9 +1,10 @@
 name: 🪲 Content bug or inaccuracy with non-API docs
 description: Report typos, bugs, out-of-date content, broken links, etc.
-labels: ["bug 🐛", "content 📄"]
+labels: ["bug 🐛", "content 📄", "needs triage 🤔"]
 assignees:
   - abbycross
   - beckykd
+  - kaelynj
 
 body:
   - type: markdown

docs/api/qiskit-ibm-runtime/dev/qiskit_ibm_runtime.RuntimeJob.mdx

@@ -258,7 +258,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJob
 
   ### interim\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJob.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/utils/deprecation.py#L399-L418" signature="interim_results(decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJob.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/runtime_job.py#L399-L418" signature="interim_results(decoder=None)">
     Return the interim results of the job.
 
     **Parameters**
@@ -443,7 +443,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJob
 
   ### stream\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJob.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/utils/deprecation.py#L368-L397" signature="stream_results(callback, decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJob.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/runtime_job.py#L368-L397" signature="stream_results(callback, decoder=None)">
     Start streaming job results.
 
     **Parameters**

docs/api/qiskit-ibm-runtime/dev/qiskit_ibm_runtime.RuntimeJobV2.mdx

@@ -236,7 +236,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJobV2
 
   ### interim\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJobV2.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/utils/deprecation.py#L306-L325" signature="interim_results(decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJobV2.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/runtime_job_v2.py#L306-L325" signature="interim_results(decoder=None)">
     Return the interim results of the job.
 
     **Parameters**
@@ -379,7 +379,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJobV2
 
   ### stream\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJobV2.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/utils/deprecation.py#L275-L304" signature="stream_results(callback, decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJobV2.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/main/qiskit_ibm_runtime/runtime_job_v2.py#L275-L304" signature="stream_results(callback, decoder=None)">
     Start streaming job results.
 
     **Parameters**

docs/api/qiskit-ibm-runtime/qiskit_ibm_runtime.RuntimeJob.mdx

@@ -258,7 +258,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJob
 
   ### interim\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJob.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/utils/deprecation.py#L399-L418" signature="interim_results(decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJob.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/runtime_job.py#L399-L418" signature="interim_results(decoder=None)">
     Return the interim results of the job.
 
     **Parameters**
@@ -443,7 +443,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJob
 
   ### stream\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJob.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/utils/deprecation.py#L368-L397" signature="stream_results(callback, decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJob.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/runtime_job.py#L368-L397" signature="stream_results(callback, decoder=None)">
     Start streaming job results.
 
     **Parameters**

docs/api/qiskit-ibm-runtime/qiskit_ibm_runtime.RuntimeJobV2.mdx

@@ -236,7 +236,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJobV2
 
   ### interim\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJobV2.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/utils/deprecation.py#L306-L325" signature="interim_results(decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJobV2.interim_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/runtime_job_v2.py#L306-L325" signature="interim_results(decoder=None)">
     Return the interim results of the job.
 
     **Parameters**
@@ -379,7 +379,7 @@ python_api_name: qiskit_ibm_runtime.RuntimeJobV2
 
   ### stream\_results
 
-  <Function id="qiskit_ibm_runtime.RuntimeJobV2.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/utils/deprecation.py#L275-L304" signature="stream_results(callback, decoder=None)">
+  <Function id="qiskit_ibm_runtime.RuntimeJobV2.stream_results" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/runtime_job_v2.py#L275-L304" signature="stream_results(callback, decoder=None)">
     Start streaming job results.
 
     **Parameters**

docs/api/qiskit-ibm-runtime/qiskit_ibm_runtime.fake_provider.FakeProviderForBackendV2.mdx

@@ -37,7 +37,7 @@ python_api_name: qiskit_ibm_runtime.fake_provider.FakeProviderForBackendV2
 
   ### get\_backend
 
-  <Function id="qiskit_ibm_runtime.fake_provider.FakeProviderForBackendV2.get_backend" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/utils/deprecation.py#L89-L92" signature="get_backend(name=None, **kwargs)">
+  <Function id="qiskit_ibm_runtime.fake_provider.FakeProviderForBackendV2.get_backend" github="https://github.com/Qiskit/qiskit-ibm-runtime/tree/stable/0.30/qiskit_ibm_runtime/fake_provider/fake_provider.py#L89-L92" signature="get_backend(name=None, **kwargs)">
     Return a single backend matching the specified filtering.
 
     **Return type**

docs/api/qiskit/0.19/qiskit.aqua.algorithms.VQE.mdx

@@ -15,7 +15,7 @@ python_api_name: qiskit.aqua.algorithms.VQE
 
   An instance of VQE requires defining two algorithmic sub-components: a trial state (ansatz) from Aqua’s [`variational_forms`](qiskit.aqua.components.variational_forms#module-qiskit.aqua.components.variational_forms "qiskit.aqua.components.variational_forms"), and one of the classical [`optimizers`](qiskit.aqua.components.optimizers#module-qiskit.aqua.components.optimizers "qiskit.aqua.components.optimizers"). The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the variational form, that will result in the minimum expectation value being measured of the input operator (Hamiltonian).
 
-  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/Qiskit/qiskit-tutorials-community/blob/master/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
+  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/qiskit-community/qiskit-community-tutorials/blob/51cb790aebbe1015f22c0957a108ff66eb1c9136/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
 
   The length of the *initial\_point* list value must match the number of the parameters expected by the variational form being used. If the *initial\_point* is left at the default of `None`, then VQE will look to the variational form for a preferred value, based on its given initial state. If the variational form returns `None`, then a random point will be generated within the parameter bounds set, as per above. If the variational form provides `None` as the lower bound, then VQE will default it to $-2\pi$; similarly, if the variational form returns `None` as the upper bound, the default value will be $2\pi$.
 

docs/api/qiskit/0.24/qiskit.aqua.algorithms.VQE.mdx

@@ -17,7 +17,7 @@ python_api_name: qiskit.aqua.algorithms.VQE
 
   An instance of VQE requires defining two algorithmic sub-components: a trial state (ansatz) from Aqua’s [`variational_forms`](qiskit.aqua.components.variational_forms#module-qiskit.aqua.components.variational_forms "qiskit.aqua.components.variational_forms"), and one of the classical [`optimizers`](qiskit.aqua.components.optimizers#module-qiskit.aqua.components.optimizers "qiskit.aqua.components.optimizers"). The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the variational form, that will result in the minimum expectation value being measured of the input operator (Hamiltonian).
 
-  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/Qiskit/qiskit-tutorials-community/blob/master/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
+  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/qiskit-community/qiskit-community-tutorials/blob/51cb790aebbe1015f22c0957a108ff66eb1c9136/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
 
   The length of the *initial\_point* list value must match the number of the parameters expected by the variational form being used. If the *initial\_point* is left at the default of `None`, then VQE will look to the variational form for a preferred value, based on its given initial state. If the variational form returns `None`, then a random point will be generated within the parameter bounds set, as per above. If the variational form provides `None` as the lower bound, then VQE will default it to $-2\pi$; similarly, if the variational form returns `None` as the upper bound, the default value will be $2\pi$.
 

docs/api/qiskit/0.25/qiskit.algorithms.VQE.mdx

@@ -17,7 +17,7 @@ python_api_name: qiskit.algorithms.VQE
 
   An instance of VQE requires defining two algorithmic sub-components: a trial state (a.k.a. ansatz) which is a `QuantumCircuit`, and one of the classical [`optimizers`](qiskit.algorithms.optimizers#module-qiskit.algorithms.optimizers "qiskit.algorithms.optimizers"). The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the ansatz, that will result in the minimum expectation value being measured of the input operator (Hamiltonian).
 
-  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. It provides an [initial point tutorial](https://github.com/Qiskit/qiskit-tutorials-community/blob/master/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
+  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. It provides an [initial point tutorial](https://github.com/qiskit-community/qiskit-community-tutorials/blob/51cb790aebbe1015f22c0957a108ff66eb1c9136/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
 
   The length of the *initial\_point* list value must match the number of the parameters expected by the ansatz being used. If the *initial\_point* is left at the default of `None`, then VQE will look to the ansatz for a preferred value, based on its given initial state. If the ansatz returns `None`, then a random point will be generated within the parameter bounds set, as per above. If the ansatz provides `None` as the lower bound, then VQE will default it to $-2\pi$; similarly, if the ansatz returns `None` as the upper bound, the default value will be $2\pi$.
 

docs/api/qiskit/0.25/qiskit.aqua.algorithms.VQE.mdx

@@ -17,7 +17,7 @@ python_api_name: qiskit.aqua.algorithms.VQE
 
   An instance of VQE requires defining two algorithmic sub-components: a trial state (ansatz) from Aqua’s [`variational_forms`](qiskit.aqua.components.variational_forms#module-qiskit.aqua.components.variational_forms "qiskit.aqua.components.variational_forms"), and one of the classical [`optimizers`](qiskit.aqua.components.optimizers#module-qiskit.aqua.components.optimizers "qiskit.aqua.components.optimizers"). The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the variational form, that will result in the minimum expectation value being measured of the input operator (Hamiltonian).
 
-  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/Qiskit/qiskit-tutorials-community/blob/master/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
+  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. Aqua provides an [initial point tutorial](https://github.com/qiskit-community/qiskit-community-tutorials/blob/51cb790aebbe1015f22c0957a108ff66eb1c9136/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
 
   The length of the *initial\_point* list value must match the number of the parameters expected by the variational form being used. If the *initial\_point* is left at the default of `None`, then VQE will look to the variational form for a preferred value, based on its given initial state. If the variational form returns `None`, then a random point will be generated within the parameter bounds set, as per above. If the variational form provides `None` as the lower bound, then VQE will default it to $-2\pi$; similarly, if the variational form returns `None` as the upper bound, the default value will be $2\pi$.
 

docs/api/qiskit/0.26/qiskit.algorithms.VQE.mdx

@@ -17,7 +17,7 @@ python_api_name: qiskit.algorithms.VQE
 
   An instance of VQE requires defining two algorithmic sub-components: a trial state (a.k.a. ansatz) which is a `QuantumCircuit`, and one of the classical [`optimizers`](qiskit.algorithms.optimizers#module-qiskit.algorithms.optimizers "qiskit.algorithms.optimizers"). The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the ansatz, that will result in the minimum expectation value being measured of the input operator (Hamiltonian).
 
-  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. It provides an [initial point tutorial](https://github.com/Qiskit/qiskit-tutorials-community/blob/master/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
+  An optional array of parameter values, via the *initial\_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. It provides an [initial point tutorial](https://github.com/qiskit-community/qiskit-community-tutorials/blob/51cb790aebbe1015f22c0957a108ff66eb1c9136/chemistry/h2_vqe_initial_point.ipynb) detailing this use case.
 
   The length of the *initial\_point* list value must match the number of the parameters expected by the ansatz being used. If the *initial\_point* is left at the default of `None`, then VQE will look to the ansatz for a preferred value, based on its given initial state. If the ansatz returns `None`, then a random point will be generated within the parameter bounds set, as per above. If the ansatz provides `None` as the lower bound, then VQE will default it to $-2\pi$; similarly, if the ansatz returns `None` as the upper bound, the default value will be $2\pi$.