update all broken links to point to github.io docs

Author: abbycross

docs/guides/addons.mdx

@@ -9,26 +9,23 @@ description: Understand the Qiskit addon tools, which help you build utility-gra
 
 Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These capabilities build on Qiskit's performant foundation of tools for creating and running quantum algorithms. They are provided as modular software components that can plug into a [workflow](/docs/guides/intro-to-patterns) to scale or design new quantum algorithms.
 
-Many of these addons are also powered by the `qiskit-addon-utils` package. You can find more information about this supplemental package in [this topic](/docs/guides/qiskit-addons-utils).
-
-## Addons for mapping
-
+Many of these addons are also powered by the `qiskit-addon-utils` package. For more information, see the [documentation](https://qiskit.github.io/qiskit-addon-utils/) for this supplemental package.
 
 <CardGroup>
     <Card
       title="AQC-Tensor"
       description="Approximate quantum compilation with tensor networks (AQC-Tensor) enables the construction of high-fidelity circuits with reduced depth."
-      href="/docs/guides/qiskit-addons-aqc"
-      analyticsName="Overview page: AQC-Tensor"
-      linkText="Overview"
+      href="https://qiskit.github.io/qiskit-addon-aqc-tensor/"
+      analyticsName="Documentation page: AQC-Tensor"
+      linkText="Browse documentation"
     />
 
     <Card
       title="Multi-product formulas"
       description="Multi-product formulas (MPF) reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions."
-      href="/docs/guides/qiskit-addons-mpf"
+      href="https://qiskit.github.io/qiskit-addon-mpf/"
       analyticsName="Documentation page: MPF"
-      linkText="Overview"
+      linkText="Browse documentation"
     />
 
     <Card
@@ -40,17 +37,13 @@ Many of these addons are also powered by the `qiskit-addon-utils` package. You c
     />
 </CardGroup>
 
-
-## Addons for optimizing workloads
-
-
 <CardGroup>
     <Card
       title="Operator backpropagation"
       description="Operator backpropagation (OBP) reduces circuit depth by trimming operations from the end at the cost of more operator measurements."
-      href="/docs/guides/qiskit-addons-obp"
-      analyticsName="Overview page: OBP"
-      linkText="Overview"
+      href="https://qiskit.github.io/qiskit-addon-obp/"
+      analyticsName="Documentation page: OBP"
+      linkText="Browse documentation"
     />
 
     <Card
@@ -62,9 +55,6 @@ Many of these addons are also powered by the `qiskit-addon-utils` package. You c
     />
 </CardGroup>
 
-
-## Addons for error mitigation
-
 <CardGroup>
     <Card
       title="Matrix-free Measurement Mitigation"
@@ -90,9 +80,6 @@ Many of these addons are also powered by the `qiskit-addon-utils` package. You c
     />
 </CardGroup>
 
-##  Addons for post-processing
-
-
 <CardGroup>
     <Card
       title="Sample-based quantum diagonalization"

docs/guides/classical-feedforward-and-control-flow.ipynb

@@ -443,7 +443,7 @@
     "     <Admonition type=\"note\">\n",
     "      You can use the [`BitArray`](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.primitives.BitArray#expectation_values) primitive attribute to compute the expectation values of the provided observables.\n",
     "     </Admonition>\n",
-    "2. Execute one basis change circuit per partition (whichever basis change needs to be done for each partition). See the Measurement bases addon utility  [`measurement_bases` module](https://github.com/Qiskit/qiskit-addon-utils/blob/38ea05431f2e9830adf4ec9265f6d19758a32096/qiskit_addon_utils/exp_vals/measurement_bases.py) for more information. [Get started with utilities.](/docs/guides/qiskit-addons-utils#get-started-with-utilities)\n",
+    "2. Execute one basis change circuit per partition (whichever basis change needs to be done for each partition). See the Measurement bases addon utility [`measurement_bases` module](https://qiskit.github.io/qiskit-addon-utils/apidocs/qiskit_addon_utils.exp_vals.html#qiskit_addon_utils.exp_vals.get_measurement_bases) for more information. For more information, see the [documentation](https://qiskit.github.io/qiskit-addon-utils/) for the Qiskit addon utilities package.\n",
     "3. Add back together the results for each partition."
    ]
   },

docs/guides/function-template-hamiltonian-simulation.ipynb

@@ -29,7 +29,7 @@
    "id": "b51e81bf-0bbf-4f64-af1e-87fcb443d997",
    "metadata": {},
    "source": [
-    "This template encapsulates a workflow to simulate the time evolution of an initial state against a user defined spin-based Hamiltonian and returns a set of specified expectation values using the [AQC addon](./qiskit-addons-aqc).\n",
+    "This template encapsulates a workflow to simulate the time evolution of an initial state against a user defined spin-based Hamiltonian and returns a set of specified expectation values using the [AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) Qiskit addon.\n",
     "\n",
     "This template is structured as a Qiskit pattern with the following steps:\n",
     "\n",
@@ -55,9 +55,9 @@
    "source": [
     "## Write the function template\n",
     "\n",
-    "First, write a function template for Hamiltonian simulation that uses the [AQC-Tensor Qiskit addon](/docs/guides/qiskit-addons-aqc) to map the problem description to a reduced-depth circuit for execution on hardware.\n",
+    "First, write a function template for Hamiltonian simulation that uses the [AQC-Tensor Qiskit addon](https://qiskit.github.io/qiskit-addon-aqc-tensor/) to map the problem description to a reduced-depth circuit for execution on hardware.\n",
     "\n",
-    "Throughout, the code is saved to `./source_files/template_hamiltonian_simulation.py`. This file is the function template you can upload to and run remotely with Qiskit Serverless."
+    "Throughout, the code is saved to `./source_files/template_hamiltonian_simulation.py`. This file is the function template you can upload to and run remotely with Qiskit Serverless."
    ]
   },
   {

docs/guides/qiskit-function-templates.mdx

@@ -15,7 +15,7 @@ There are two types of templates:
 
 ## Template implementations
 
-Qiskit Function template implementations are organized by application area. Currently included in the collection is a physics template for Hamiltonian simulation using the [AQC-Tensor Qiskit addon](./qiskit-addons-aqc) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](./qiskit-addons-sqd).
+Qiskit Function template implementations are organized by application area. Currently included in the collection is a physics template for Hamiltonian simulation using the [AQC-Tensor Qiskit addon](https://qiskit.github.io/qiskit-addon-aqc-tensor/) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](/docs/guides/qiskit-addons-sqd).
 Resources to get started with these two templates are available at the following links:
 
 - Electronic structure simulation with implicit solvent model: [template source files](https://github.com/qiskit-community/qiskit-function-templates/tree/main/chemistry/sqd_pcm) and [guide](./function-template-chemistry-workflow)

docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "This tutorial demonstrates how to implement **Approximate Quantum Compilation** using tensor networks (AQC-Tensor) with Qiskit to enhance quantum circuit performance. We apply AQC-Tensor within the context of a Trotterized time evolution to reduce circuit depth while maintaining simulation accuracy, following the Qiskit framework for state preparation and optimization. You will learn how to create a low-depth ansatz circuit from an initial Trotter circuit, optimize it with tensor networks, and prepare it for quantum hardware execution.\n",
     "\n",
-    "The primary objective is to simulate time evolution for a model Hamiltonian with a reduced circuit depth. This is achieved using the **AQC-Tensor** Qiskit addon, [qiskit-addon-aqc-tensor](https://github.com/Qiskit/qiskit-addon-aqc-tensor), which leverages tensor networks, specifically matrix product states (MPS), to compress and optimize the initial circuit. Through iterative adjustments, the compressed ansatz circuit maintains fidelity to the original circuit while staying feasible for near-term quantum hardware. More details can be found in the corresponding [docs](/docs/guides/qiskit-addons-aqc) with a [simple example](/docs/guides/qiskit-addons-aqc-get-started) to get started.\n",
+    "The primary objective is to simulate time evolution for a model Hamiltonian with a reduced circuit depth. This is achieved using the **AQC-Tensor** Qiskit addon, [qiskit-addon-aqc-tensor](https://github.com/Qiskit/qiskit-addon-aqc-tensor), which leverages tensor networks, specifically matrix product states (MPS), to compress and optimize the initial circuit. Through iterative adjustments, the compressed ansatz circuit maintains fidelity to the original circuit while staying feasible for near-term quantum hardware. See the [documentation](https://qiskit.github.io/qiskit-addon-aqc-tensor/) for more information.\n",
     "\n",
     "Approximate Quantum Compilation is particularly advantageous in quantum simulations that exceed hardware coherence times, as it allows complex simulations to be performed more efficiently. This tutorial guides you through the AQC-Tensor workflow setup in Qiskit, covering initialization of a Hamiltonian, generation of Trotter circuits, and transpilation of the final optimized circuit for a target device."
    ]

docs/tutorials/operator-back-propagation.ipynb

@@ -40,7 +40,7 @@
    "source": [
     "## Background\n",
     "\n",
-    "Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into the measured observable, generally reducing the depth of the circuit at the cost of additional terms in the observable. The goal is to backpropagate as much of the circuit as possible without allowing the observable to grow too large. A Qiskit-based implementation is available in the OBP Qiskit addon, more details can be found in the corresponding [docs](/docs/guides/qiskit-addons-obp) with a [simple example](/docs/guides/qiskit-addons-obp-get-started) to get started.\n",
+    "Operator backpropagation is a technique which involves absorbing operations from the end of a quantum circuit into the measured observable, generally reducing the depth of the circuit at the cost of additional terms in the observable. The goal is to backpropagate as much of the circuit as possible without allowing the observable to grow too large. A Qiskit-based implementation is available in the OBP Qiskit addon. Read the corresponding [documentation](https://qiskit.github.io/qiskit-addon-obp/) for more information.\n",
     "\n",
     "Consider an example circuit for which an observable $O = \\sum_P c_P P$ is to be measured, where $P$ are Paulis and $c_P$ are coefficients. Let us denote the circuit as a single unitary $U$ which can be logically partitioned into $U = U_C U_Q$ as shown in the figure below.\n",
     "\n",

docs/tutorials/qedma-2d-ising-with-qesem.ipynb

@@ -52,7 +52,7 @@
     "* Estimate the expected QPU runtime for full error mitigation using QESEM's analytical and empirical time estimation features.\n",
     "* Construct and simulate the 2D tilted-field Ising model circuit using hardware-inspired qubit layouts and gate layers.\n",
     "* Visualize device qubit connectivity and selected subgraphs for your experiment.\n",
-    "* Demonstrate the use of [operator backpropagation (OBP)](/docs/guides/qiskit-addons-obp) to reduce circuit depth. This technique trims operations from the circuit end at the cost of more operator measurements.\n",
+    "* Demonstrate the use of [operator backpropagation (OBP)](https://qiskit.github.io/qiskit-addon-obp/) to reduce circuit depth. This technique trims operations from the circuit end at the cost of more operator measurements.\n",
     "* Perform unbiased error mitigation (EM) for multiple observables simultaneously by using QESEM, comparing ideal, noisy, and mitigated results.\n",
     "* Analyze and plot the impact of error mitigation on magnetization across different circuit depths.\n",
     "\n",
@@ -808,7 +808,7 @@
    "id": "75dbab74",
    "metadata": {},
    "source": [
-    "Now we will use operator backpropagation (OBP). (See the [OBP](/docs/guides/qiskit-addons-obp) guide for more details on the OBP Qiskit addon.) We will create a function that generates the circuit slices for backpropagation:"
+    "Now we will use operator backpropagation (OBP). (See the [OBP](https://qiskit.github.io/qiskit-addon-obp/) documentation for more details on the OBP Qiskit addon.) We will create a function that generates the circuit slices for backpropagation:"
    ]
   },
   {