diff --git a/.gitignore b/.gitignore
index e7befa878..523b7c4a7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@
_build
__pycache__/
docs/build/
+docs/source/tags/
mitiq.egg-info/
dist/
build/
diff --git a/docs/CONTRIBUTING_DOCS.md b/docs/CONTRIBUTING_DOCS.md
index 088351b20..4d3af7351 100644
--- a/docs/CONTRIBUTING_DOCS.md
+++ b/docs/CONTRIBUTING_DOCS.md
@@ -212,7 +212,7 @@ To call sphinx directly, `cd` into the `docs` directory and run
sphinx-build -b html source build
```
-These commands generate the `docs/build` folder, which is ignore by the `.gitignore` file.
+These commands generate the `docs/build` folder, which is ignored by the `.gitignore` file.
Once the documentation is generated you can view it by opening it in your browser.
## Testing the Documentation
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 98f47b4b9..0f52e1c76 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -98,8 +98,42 @@ def setup(app):
"sphinx_copybutton",
"nbsphinx",
"sphinx_gallery.load_style",
+ "sphinx_design",
+ "sphinx_tags",
]
+# to add tags to the documentation tutorials
+tags_create_tags = True
+tags_output_dir = "tags/"
+tags_overview_title = "All tags"
+tags_create_badges = True
+tags_intro_text = "Tags on this page: "
+tags_page_title = "Tags"
+tags_page_header = "Pages with this tag: "
+tags_index_head = "Tags in the documentation tutorials: "
+tags_extension = ["md"]
+tags_badge_colors = {
+ "zne": "primary",
+ "rem": "primary",
+ "shadows": "primary",
+ "cdr": "primary",
+ "pec": "primary",
+ "ddd": "primary",
+ "calibration": "primary",
+ "cirq": "secondary",
+ "bqskit": "secondary",
+ "braket": "secondary",
+ "pennylane": "secondary",
+ "qiskit": "secondary",
+ "stim": "secondary",
+ "qrack": "secondary",
+ "qibo": "secondary",
+ "ionq": "secondary",
+ "basic": "success",
+ "intermediate": "success",
+ "advanced": "success",
+}
+
# hide primary sidebar from the following pages
html_sidebars = {"apidoc": [], "changelog": [], "bibliography": []}
diff --git a/docs/source/examples/bqskit.md b/docs/source/examples/bqskit.md
index 04b4d7181..e9a399fca 100644
--- a/docs/source/examples/bqskit.md
+++ b/docs/source/examples/bqskit.md
@@ -10,6 +10,9 @@ kernelspec:
name: python3
---
+```{tags} bqskit, zne, intermediate
+```
+
# Improving the accuracy of BQSKit compiled circuits with error mitigation
In this tutorial we describe how to use error mitigation capabilities from [Mitiq](https://mitiq.readthedocs.io/en/stable/) together with the compilation capabilities of [BQSKit](https://bqskit.lbl.gov/) {cite}`Patel_2022_ACM`, a compiler for quantum circuits. BQSKit stands for Berkeley Quantum Synthesis Toolkit and it allows one "to compile quantum programs to efficient physical circuits for any QPU".
diff --git a/docs/source/examples/braket_mirror_circuit.md b/docs/source/examples/braket_mirror_circuit.md
index ee122dfd7..10e86c2fe 100644
--- a/docs/source/examples/braket_mirror_circuit.md
+++ b/docs/source/examples/braket_mirror_circuit.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} braket, zne, basic
+```
+
# Mitiq with Braket
This notebook shows improved performance on a mirror circuit benchmark with zero-noise extrapolation on Rigetti Aspen-9 via Amazon Braket.
diff --git a/docs/source/examples/calibration-tutorial.md b/docs/source/examples/calibration-tutorial.md
index 83f401d29..06f9b1570 100644
--- a/docs/source/examples/calibration-tutorial.md
+++ b/docs/source/examples/calibration-tutorial.md
@@ -12,6 +12,9 @@ kernelspec:
name: python3
---
+```{tags} calibration, zne, qiskit, basic
+```
+
# Breaking into error mitigation with Mitiq's calibration module
diff --git a/docs/source/examples/cdr_qrack.md b/docs/source/examples/cdr_qrack.md
index cdcbe8788..6f9aa5a2e 100644
--- a/docs/source/examples/cdr_qrack.md
+++ b/docs/source/examples/cdr_qrack.md
@@ -12,6 +12,9 @@ kernelspec:
name: python3
---
+```{tags} qiskit, cdr, qrack, cirq, basic
+```
+
# CDR with Qrack as Near-Clifford Simulator
In this tutorial, [Clifford Data Regression](../guide/cdr.md) (CDR) is used with [Qrack](https://qrack.readthedocs.io/en/latest/) and a Qiskit fake backend.
diff --git a/docs/source/examples/cirq-ibmq-backends.md b/docs/source/examples/cirq-ibmq-backends.md
index 90fb133e8..391f9d631 100644
--- a/docs/source/examples/cirq-ibmq-backends.md
+++ b/docs/source/examples/cirq-ibmq-backends.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} cirq, zne, qiskit, basic
+```
+
# Error mitigation with Cirq on IBMQ backends
diff --git a/docs/source/examples/combine_ddd_zne.md b/docs/source/examples/combine_ddd_zne.md
index c907ee290..9078ce108 100644
--- a/docs/source/examples/combine_ddd_zne.md
+++ b/docs/source/examples/combine_ddd_zne.md
@@ -11,6 +11,8 @@ kernelspec:
language: python
name: python3
---
+```{tags} ddd, zne, cirq, intermediate
+```
# Composing techniques: Digital Dynamical Decoupling and Zero Noise Extrapolation
diff --git a/docs/source/examples/combine_rem_zne.md b/docs/source/examples/combine_rem_zne.md
index 6676418d4..21a9c018d 100644
--- a/docs/source/examples/combine_rem_zne.md
+++ b/docs/source/examples/combine_rem_zne.md
@@ -11,6 +11,8 @@ kernelspec:
language: python
name: python3
---
+```{tags} rem, zne, cirq, intermediate
+```
# Composing techniques: Readout Error Mitigation and Zero Noise Extrapolation
diff --git a/docs/source/examples/ddd_on_ibmq_ghz.md b/docs/source/examples/ddd_on_ibmq_ghz.md
index fa3c29a5d..9d432203e 100644
--- a/docs/source/examples/ddd_on_ibmq_ghz.md
+++ b/docs/source/examples/ddd_on_ibmq_ghz.md
@@ -11,6 +11,8 @@ kernelspec:
language: python
name: python3
---
+```{tags} ddd, qiskit, basic
+```
# Digital dynamical decoupling (DDD) with Qiskit on GHZ Circuits
diff --git a/docs/source/examples/ddd_tutorial.md b/docs/source/examples/ddd_tutorial.md
index 96ef6344a..c2df39583 100644
--- a/docs/source/examples/ddd_tutorial.md
+++ b/docs/source/examples/ddd_tutorial.md
@@ -10,6 +10,8 @@ kernelspec:
language: python
name: python3
---
+```{tags} ddd, cirq, basic
+```
# Digital dynamical decoupling (DDD) with Mirror Circuits
diff --git a/docs/source/examples/examples.md b/docs/source/examples/examples.md
index 860d4f99f..adbd1c940 100644
--- a/docs/source/examples/examples.md
+++ b/docs/source/examples/examples.md
@@ -1,6 +1,8 @@
# Examples
-Below you can find a gallery of tutorials applying Zero Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Digital Dynamical Decoupling (DDD) with Mitiq:
+Below you can find a gallery of tutorials applying Zero Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Digital Dynamical Decoupling (DDD), etc. with Mitiq.
+
+You can also search for an example utilizing a specific QEM technique, frontend, backend or difficulty level by clicking through the tags available [here.](tags.md)
```{nbgallery}
ZNE Calibration with Qiskit
@@ -37,4 +39,4 @@ GGI Summer School ZNE Hands-On Tutorial
Composing techniques: REM + ZNE
Composing techniques: DDD + ZNE
The Mitiq paper code
-```
+```
\ No newline at end of file
diff --git a/docs/source/examples/ggi_summer_school_unsolved.md b/docs/source/examples/ggi_summer_school_unsolved.md
index 85e67a7c8..f63662303 100644
--- a/docs/source/examples/ggi_summer_school_unsolved.md
+++ b/docs/source/examples/ggi_summer_school_unsolved.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} qiskit, zne, intermediate
+```
+
# Hands-on lab on error mitigation with Mitiq.
+++
diff --git a/docs/source/examples/hamiltonians.md b/docs/source/examples/hamiltonians.md
index 6987d7ccb..25f17a494 100644
--- a/docs/source/examples/hamiltonians.md
+++ b/docs/source/examples/hamiltonians.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} cirq, zne, advanced
+```
+
# Defining Hamiltonians as Linear Combinations of Pauli Strings
This tutorial shows an example of using Hamiltonians defined as {class}`~cirq.PauliSum` objects or similar objects in other
diff --git a/docs/source/examples/ibmq-backends.md b/docs/source/examples/ibmq-backends.md
index 63e4890cc..2cd750bee 100644
--- a/docs/source/examples/ibmq-backends.md
+++ b/docs/source/examples/ibmq-backends.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} qiskit, zne, basic
+```
+
# Error mitigation on IBMQ backends with Qiskit
diff --git a/docs/source/examples/layerwise-folding.md b/docs/source/examples/layerwise-folding.md
index b2b081ddb..6e5efd0a9 100644
--- a/docs/source/examples/layerwise-folding.md
+++ b/docs/source/examples/layerwise-folding.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} qiskit, zne, intermediate
+```
+
# ZNE with Qiskit: Layerwise folding
diff --git a/docs/source/examples/learning-depolarizing-noise.md b/docs/source/examples/learning-depolarizing-noise.md
index 2419e1c3b..0f2756dea 100644
--- a/docs/source/examples/learning-depolarizing-noise.md
+++ b/docs/source/examples/learning-depolarizing-noise.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} cirq, pec, advanced
+```
+
# Learning quasiprobability representations with a depolarizing noise model
In this example, we demonstrate the workflow of learning quasiprobability representations of a `CNOT` gate with a depolarizing noise model,
diff --git a/docs/source/examples/maxcut-demo.md b/docs/source/examples/maxcut-demo.md
index 9450e3fc8..fed240089 100644
--- a/docs/source/examples/maxcut-demo.md
+++ b/docs/source/examples/maxcut-demo.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} cirq, zne, advanced
+```
+
# Solving MaxCut with Mitiq-improved QAOA
+++
diff --git a/docs/source/examples/molecular_hydrogen.md b/docs/source/examples/molecular_hydrogen.md
index 95f45e2a5..defa5c55d 100644
--- a/docs/source/examples/molecular_hydrogen.md
+++ b/docs/source/examples/molecular_hydrogen.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} cirq, zne, advanced
+```
+
# Estimating the potential energy surface of molecular Hydrogen with ZNE and OpenFermion
In this example we apply zero-noise extrapolation (ZNE) to the estimation of the potential energy surface of molecular Hydrogen ($\rm H_2$).
diff --git a/docs/source/examples/molecular_hydrogen_pennylane.md b/docs/source/examples/molecular_hydrogen_pennylane.md
index 720ab8850..914f20c80 100644
--- a/docs/source/examples/molecular_hydrogen_pennylane.md
+++ b/docs/source/examples/molecular_hydrogen_pennylane.md
@@ -10,6 +10,8 @@ kernelspec:
language: python
name: python3
---
+```{tags} cirq, pennylane, zne, advanced
+```
# Estimating the potential energy surface of molecular Hydrogen with ZNE and PennyLane + Cirq
diff --git a/docs/source/examples/pec_tutorial.md b/docs/source/examples/pec_tutorial.md
index 299d9329b..22b9b32ea 100644
--- a/docs/source/examples/pec_tutorial.md
+++ b/docs/source/examples/pec_tutorial.md
@@ -11,6 +11,8 @@ kernelspec:
name: python3
---
+```{tags} pec, cirq, qiskit, basic
+```
# Probabilistic error cancellation (PEC) with Mirror Circuits
This notebook shows probabilistic error cancellation (PEC) *Temme et al. PRL (2017)* {cite}`Temme_2017_PRL`, *Sun et al. PRLAppl (2021)* {cite}`Sun_2021_PRAppl`, *Zhang et al. NatComm (2020)* {cite}`Zhang_2020_NatComm`, improving performance of a mirror circuit benchmark on the `braket_dm` noisy simulator.
diff --git a/docs/source/examples/pennylane-ibmq-backends.md b/docs/source/examples/pennylane-ibmq-backends.md
index d68f44793..feb5ba654 100644
--- a/docs/source/examples/pennylane-ibmq-backends.md
+++ b/docs/source/examples/pennylane-ibmq-backends.md
@@ -11,6 +11,9 @@ kernelspec:
name: python3
---
+```{tags} qiskit, zne, pennylane, basic
+```
+
# Error mitigation with Pennylane on IBMQ backends
In this tutorial we will cover how to use Mitiq in conjunction with [PennyLane](https://pennylane.ai/), and further how to run error-mitigated circuits on IBMQ backends.
diff --git a/docs/source/examples/pyquil_demo.ipynb b/docs/source/examples/pyquil_demo.ipynb
index 2f9b654df..94ec38e5d 100644
--- a/docs/source/examples/pyquil_demo.ipynb
+++ b/docs/source/examples/pyquil_demo.ipynb
@@ -1,5 +1,18 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e564c033",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "```{tags} zne, intermediate\n",
+ "```"
+ ]
+ },
{
"cell_type": "markdown",
"id": "dac81d40",
@@ -141,7 +154,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
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