Jessie Yu (jyu00) · Oct 8, 2026 · Oct 8, 2026 · Oct 8, 2026 · Oct 13, 2026 · Oct 13, 2026
diff --git a/docs/addons/pauli-prop/_toc.json b/docs/addons/pauli-prop/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-management-techniques",
   "parentLabel": "Documentation",
   "title": "Pauli propagation 0.2",
   "collapsed": true,

diff --git a/docs/addons/qiskit-addon-obp/_toc.json b/docs/addons/qiskit-addon-obp/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-management-techniques",
   "parentLabel": "Documentation",
   "title": "Operator backpropagation (OBP) 0.3",
   "collapsed": true,

diff --git a/docs/addons/qiskit-addon-paulice/_toc.json b/docs/addons/qiskit-addon-paulice/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-management-techniques",
   "parentLabel": "Documentation",
   "title": "Qiskit Paulice 0.2",
   "collapsed": true,

diff --git a/docs/addons/qiskit-addon-pna/_toc.json b/docs/addons/qiskit-addon-pna/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-management-techniques",
   "parentLabel": "Documentation",
   "title": "Qiskit propagated noise absorption 0.2",
   "collapsed": true,

diff --git a/docs/addons/qiskit-addon-pna/guides/quickstart.ipynb b/docs/addons/qiskit-addon-pna/guides/quickstart.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "This guide demonstrates a minimal working example of the `qiskit-addon-pna` package. We use propagated noise absorption (PNA) to build a noise-mitigating observable. Given a circuit and a Pauli-Lindblad noise model, PNA classically propagates the observable through the inverse noise channel. Measuring the resulting observable on the noisy QPU mitigates the learned gate noise.\n",
     "\n",
-    "To see how to build a realistic workflow and run on quantum hardware with the [directed execution model](/docs/guides/directed-execution-model), including learning the noise model with `NoiseLearnerV3`, check out the [PNA tutorial](/docs/tutorials/propagated-noise-absorption) on the IBM Quantum Platform."
+    "To see how to build a realistic workflow and run on quantum hardware with the [directed execution model](/docs/ibm-quantum-compute/directed-execution-model), including learning the noise model with `NoiseLearnerV3`, check out the [PNA tutorial](/docs/tutorials/propagated-noise-absorption) on the IBM Quantum Platform."
    ]
   },
   {

diff --git a/docs/addons/qiskit-addon-slc/_toc.json b/docs/addons/qiskit-addon-slc/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-modeling",
   "parentLabel": "Documentation",
   "title": "Shaded lightcones 0.2",
   "collapsed": true,

diff --git a/docs/addons/qiskit-addon-slc/index.mdx b/docs/addons/qiskit-addon-slc/index.mdx
@@ -36,7 +36,7 @@ Shaded lightcones are calculated and used in 5 steps:
 
 ![A row of numbered error terms sorted from most to least impactful, starting with term 8 and ending with terms 1, 2, 10 and 12. The last five terms, shaded darkest, are separated from the rest and labeled truncate.](/docs/images/addons/qiskit-addon-slc/prioritize_and_truncate.avif)
 
-5.  Mitigate the truncated noise model. Using the [directed execution model](/docs/guides/directed-execution-model) from Qiskit Runtime, specifying error terms to ignore during PEC sampling is straightforward. See the [tutorial](/docs/tutorials/pec-with-shaded-lightcones) for details.
+5.  Mitigate the truncated noise model. Using the [directed execution model](/docs/ibm-quantum-compute/directed-execution-model) from Qiskit Runtime, specifying error terms to ignore during PEC sampling is straightforward. See the [tutorial](/docs/tutorials/pec-with-shaded-lightcones) for details.
 
 ![The prioritized list of retained error terms, 8, 11, 7, 5, 6, 3 and 4, with an arrow pointing to a three-qubit circuit. Each retained term appears in the circuit as an inverse noise gate, labeled lambda inverse, between the unitary layers.](/docs/images/addons/qiskit-addon-slc/reduced_pec.avif)
 

diff --git a/docs/addons/qiskit-addon-sqd/_toc.json b/docs/addons/qiskit-addon-sqd/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/hamiltonian-simulation",
   "parentLabel": "Documentation",
   "title": "Sample-based quantum diagonalization (SQD) 0.13",
   "collapsed": true,

diff --git a/docs/addons/qiskit-fermions/_toc.json b/docs/addons/qiskit-fermions/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/hamiltonian-simulation",
   "parentLabel": "Documentation",
   "title": "Qiskit Fermions 0.3",
   "collapsed": true,

diff --git a/docs/addons/qiskit-fermions/guides/skqd.mdx b/docs/addons/qiskit-fermions/guides/skqd.mdx
@@ -439,7 +439,7 @@ This closes the SKQD loop. The momentum-basis circuits of steps 1–5 produce a
 
 ## Next steps
 
-This guide ran the whole SKQD pipeline on a noiseless statevector simulator. On hardware, the transpiled `circuits` would be executed and measured in place of the statevector sampling in step 6, with the resulting counts feeding the step-7 diagonalization unchanged. Refer to the [Qiskit documentation](/docs/guides/intro-to-patterns) for help running circuits, and to the [SQD addon tutorials](/docs/addons/qiskit-addon-sqd/guides/overview) for information about the subspace-diagonalization post-processing, including its behavior on noisy samples, where configuration recovery does the most work.
+This guide ran the whole SKQD pipeline on a noiseless statevector simulator. On hardware, the transpiled `circuits` would be executed and measured in place of the statevector sampling in step 6, with the resulting counts feeding the step-7 diagonalization unchanged. Refer to the [Qiskit documentation](/docs/ibm-quantum-compute/hello-world) for help running circuits, and to the [SQD addon tutorials](/docs/addons/qiskit-addon-sqd/guides/overview) for information about the subspace-diagonalization post-processing, including its behavior on noisy samples, where configuration recovery does the most work.
 
 Read the [ffsim relationship guide](ffsim#ffsim-relationship-explanation) to understand why [`ffsim.apply_unitary()`](https://qiskit-community.github.io/ffsim/api/stubs/ffsim.apply_unitary.html#ffsim.apply_unitary "(in ffsim)") and [`ffsim.sample_state_vector()`](https://qiskit-community.github.io/ffsim/api/stubs/ffsim.sample_state_vector.html#ffsim.sample_state_vector "(in ffsim)") work natively on this package’s fermionic circuits, and why the fixed-particle-number sector used in step 6 makes the momentum-basis sparsity established in step 2 sample-efficient.
 
diff --git a/docs/addons/qiskit-fermions/guides/sqdrift.mdx b/docs/addons/qiskit-fermions/guides/sqdrift.mdx
@@ -409,7 +409,7 @@ You can add an additional optimization step to the transpilation pipeline that m
 
 ## Next steps
 
-Now that you have successfully generated an ensemble of circuits, you can sample bitstrings from them. To do so, the circuits must be executed on hardware. Refer to the [Qiskit documentation](/docs/guides/intro-to-patterns) for detailed instructions.
+Now that you have successfully generated an ensemble of circuits, you can sample bitstrings from them. To do so, the circuits must be executed on hardware. Refer to the [Qiskit documentation](/docs/ibm-quantum-compute/hello-world) for detailed instructions.
 
 Once the bitstring samples have been obtained, these can be used in combination with the Hamiltonian coefficients to perform SQD post-processing, as explained in the [SQD addon tutorials](/docs/addons/qiskit-addon-sqd/guides/overview).
 
diff --git a/docs/addons/qiskit-mitigation/_toc.json b/docs/addons/qiskit-mitigation/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-management-techniques",
   "parentLabel": "Documentation",
   "title": "Qiskit Mitigation 0.1",
   "collapsed": true,

diff --git a/docs/addons/qiskit-mitigation/guides/gate-folding-with-custom-extrapolation.ipynb b/docs/addons/qiskit-mitigation/guides/gate-folding-with-custom-extrapolation.ipynb
@@ -175,7 +175,7 @@
    "source": [
     "### Compute noisy, unmitigated expectation values\n",
     "\n",
-    "A ``QuantumProgram`` contains a quantum circuit and a samplex in a format suitable for the [Executor primitive](/docs/guides/get-started-with-executor). `MitigationTask.prepare` builds a ``QuantumProgram`` from the circuit, the observables, and the parameter values, and boxes the circuit with the given boxing options. You set the ``measure_annotations`` boxing option to ``\"change_basis\"``, which rotates the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the boxes of the circuit. The program is Pauli-twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot budget per circuit.\n",
+    "A ``QuantumProgram`` contains a quantum circuit and a samplex in a format suitable for the [Executor primitive](/docs/ibm-quantum-compute/get-started-with-executor). `MitigationTask.prepare` builds a ``QuantumProgram`` from the circuit, the observables, and the parameter values, and boxes the circuit with the given boxing options. You set the ``measure_annotations`` boxing option to ``\"change_basis\"``, which rotates the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the boxes of the circuit. The program is Pauli-twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot budget per circuit.\n",
     "\n",
     "A user with QPU access would execute the program with an `Executor` built from a backend of the `QiskitRuntimeService`. Here, you will pass an `AerSimulator` to the `Executor`, which runs the program locally, and use its `layer_noise_model` simulator option to inject a random Pauli-Lindblad noise model on every layer of entangling gates, generated with `random_pauli_lindblad_noise` from `qiskit-mitigation` with an error per layered gate (EPLG) of ``0.003``, typical of current Heron QPUs."
    ]

diff --git a/docs/addons/qiskit-mitigation/guides/pea-with-noise-learning.ipynb b/docs/addons/qiskit-mitigation/guides/pea-with-noise-learning.ipynb
@@ -209,7 +209,7 @@
    "source": [
     "### Compute noisy, unmitigated expectation values\n",
     "\n",
-    "A ``QuantumProgram`` contains a quantum circuit and a samplex in a format suitable for the [Executor primitive](/docs/guides/get-started-with-executor). `MitigationTask.prepare` builds a ``QuantumProgram`` from the circuit, the observables, and the parameter values, and boxes the circuit with the given boxing options.Set the ``measure_annotations`` boxing option to ``\"change_basis\"``, which rotates the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the boxes of the circuit. The program is Pauli-twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled, and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot budget per circuit.\n",
+    "A ``QuantumProgram`` contains a quantum circuit and a samplex in a format suitable for the [Executor primitive](/docs/ibm-quantum-compute/get-started-with-executor). `MitigationTask.prepare` builds a ``QuantumProgram`` from the circuit, the observables, and the parameter values, and boxes the circuit with the given boxing options.Set the ``measure_annotations`` boxing option to ``\"change_basis\"``, which rotates the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the boxes of the circuit. The program is Pauli-twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled, and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot budget per circuit.\n",
     "\n",
     "A user with QPU access would execute the program with an `Executor` built from a backend of the `QiskitRuntimeService`. Here you will pass an `AerSimulator` to the `Executor`, which runs the program locally, and use its `layer_noise_model` simulator option to inject a random Pauli-Lindblad noise model on every layer of entangling gates, generated with `random_pauli_lindblad_noise` from `qiskit-mitigation` with an error per layered gate (EPLG) of ``0.003``, typical of current Heron QPUs."
    ]

diff --git a/docs/addons/qiskit-mitigation/guides/pec-with-trex.ipynb b/docs/addons/qiskit-mitigation/guides/pec-with-trex.ipynb
@@ -144,7 +144,7 @@
    "source": [
     "### Compute noisy, unmitigated expectation values\n",
     "\n",
-    "A [`QuantumProgram`](/docs/guides/executor-input-output#inputs-quantum-programs) contains a quantum circuit and a [samplex](https://qiskit.github.io/samplomatic/guides/samplex_io.html) in a format suitable for the [Executor](/docs/guides/get-started-with-executor) primitive. `MitigationTask.prepare` builds a `QuantumProgram` from the circuit, the observables and the parameter values, and boxes the circuit with the given boxing options. Set the ``measure_annotations`` boxing option to ``\"change_basis\"`` to rotate the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the circuit's boxes. The program is Pauli twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot limit per circuit.\n",
+    "A [`QuantumProgram`](/docs/ibm-quantum-compute/executor-input-output#inputs-quantum-programs) contains a quantum circuit and a [samplex](https://qiskit.github.io/samplomatic/guides/samplex_io.html) in a format suitable for the [Executor](/docs/ibm-quantum-compute/get-started-with-executor) primitive. `MitigationTask.prepare` builds a `QuantumProgram` from the circuit, the observables and the parameter values, and boxes the circuit with the given boxing options. Set the ``measure_annotations`` boxing option to ``\"change_basis\"`` to rotate the measurements into the required bases, and add ``add_tags: \"unique_box\"`` so the simulator can map a noise model onto the circuit's boxes. The program is Pauli twirled by default, which on hardware turns coherent noise into the Pauli noise that the mitigation methods assume. `num_randomizations` sets how many twirled instances of each circuit are sampled and `shots_per_randomization` sets how many shots each instance receives, so their product is the shot limit per circuit.\n",
     "\n",
     "If you have QPU access, you can execute the program with an `Executor` built from a backend of the `QiskitRuntimeService`.  This examples uses an `AerSimulator` to run the program locally. Its `layer_noise_model` simulator option injects a random Pauli-Lindblad noise model on every layer of entangling gates, generated with `random_pauli_lindblad_noise` from `qiskit-mitigation` with an error per layered gate (EPLG) of ``0.003``, typical of current Heron QPUs."
    ]

diff --git a/docs/addons/qiskit-noise-learning/_toc.json b/docs/addons/qiskit-noise-learning/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides/noise-modeling",
   "parentLabel": "Documentation",
   "title": "Qiskit noise learning 0.1",
   "collapsed": true,

diff --git a/docs/addons/samplomatic/_toc.json b/docs/addons/samplomatic/_toc.json
@@ -1,5 +1,5 @@
 {
-  "parentUrl": "/docs/guides/addons",
+  "parentUrl": "/docs/guides",
   "parentLabel": "Documentation",
   "title": "Samplomatic 0.21",
   "collapsed": true,

diff --git a/docs/api/functions/_toc.json b/docs/api/functions/_toc.json
diff --git a/docs/api/functions/index.mdx b/docs/api/functions/index.mdx
-Original file line number
+Diff line change
@@ Expand Up / @@ -20,7 +20,7 @@ @@
         "\n",
         "This guide demonstrates a minimal working example of the `qiskit-addon-pna` package. We use propagated noise absorption (PNA) to build a noise-mitigating observable. Given a circuit and a Pauli-Lindblad noise model, PNA classically propagates the observable through the inverse noise channel. Measuring the resulting observable on the noisy QPU mitigates the learned gate noise.\n",
         "\n",
-        "To see how to build a realistic workflow and run on quantum hardware with the [directed execution model](/docs/guides/directed-execution-model), including learning the noise model with `NoiseLearnerV3`, check out the [PNA tutorial](/docs/tutorials/propagated-noise-absorption) on the IBM Quantum Platform."
+        "To see how to build a realistic workflow and run on quantum hardware with the [directed execution model](/docs/ibm-quantum-compute/directed-execution-model), including learning the noise model with `NoiseLearnerV3`, check out the [PNA tutorial](/docs/tutorials/propagated-noise-absorption) on the IBM Quantum Platform."
        ]
       },
       {
@@ Expand Down @@