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Adds git hook for cleaning out notebooks; uses nb-clean to clean up all the things
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‎.githooks/pre-commit‎

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#!/bin/sh
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#
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# This pre-commit hook cleans Jupyter notebooks before they are committed.
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# It uses nb-clean to remove unnecessary metadata, which helps to reduce
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# noise in commits and avoid merge conflicts.
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# Check if uv is installed. If not, warn the user and exit gracefully.
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if ! command -v uv >/dev/null 2>&1; then
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echo "-----------------------------------------------------------------"
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echo "Warning: 'uv' is not installed, so notebooks will not be cleaned."
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echo "For cleaner commits, please install uv:"
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echo "https://astral.sh/uv"
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echo "-----------------------------------------------------------------"
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exit 0
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fi
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# Get a list of staged .ipynb files.
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STAGED_NOTEBOOKS=$(git diff --cached --name-only --filter=ACM -- "*.ipynb")
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if [ -z "$STAGED_NOTEBOOKS" ]; then
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exit 0
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fi
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echo "Cleaning staged notebooks..."
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# Loop through the staged notebooks and clean them.
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for notebook in $STAGED_NOTEBOOKS; do
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echo " - $notebook"
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uvx nb-clean clean --remove-all-notebook-metadata "$notebook"
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git add "$notebook"
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done
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echo "Notebook cleaning complete."

‎README.md‎

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---
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## Git Hooks
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This repository includes a _`pre-commit` Git hook_ to automatically clean Jupyter notebook metadata using `nb-clean` before committing. This helps reduce unnecessary noise in commits and avoid merge conflicts.
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To enable this, run the following command in your terminal from the root of this repository:
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```shell
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git config core.hooksPath .githooks
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```
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**Note:** If `uv` is not installed, the hook will issue a warning and skip the cleaning process.
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---
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## Support
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For support, you can submit issues or PRs in this repository. Alternatively, you can contact us at [support@ionq.com](mailto:support@ionq.com?subject=SDK%20help).

‎cirq.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "import cirq\nimport cirq_ionq\nimport matplotlib.pyplot as plt\n\nfrom helpers import get_ionq_api_key\n\n# Before you begin, get your API key from https://cloud.ionq.com/settings/keys\n\n# If your API key is stored as \"IONQ_API_KEY\" in your local environment, this\n# should find it. Otherwise you'll be prompted to enter your API key manually.\n\napi_key = get_ionq_api_key()\n\nservice = cirq_ionq.Service(api_key=api_key, default_target='simulator')"
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"source": [
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"import cirq\n",
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"import cirq_ionq\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from helpers import get_ionq_api_key\n",
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"\n",
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"# Before you begin, get your API key from https://cloud.ionq.com/settings/keys\n",
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"\n",
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"# If your API key is stored as \"IONQ_API_KEY\" in your local environment, this\n",
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"# should find it. Otherwise you'll be prompted to enter your API key manually.\n",
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"\n",
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"api_key = get_ionq_api_key()\n",
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"\n",
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"service = cirq_ionq.Service(api_key=api_key, default_target='simulator')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0: ───H───@───M───\n",
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" │ │\n",
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"1: ───────X───M───\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Before submitting the job, we can visualize the circuit using print().\n",
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"\n",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Now we'll send the job to our backend for processing and plot the results.\n\nresult = service.run(circuit, name=\"Cirq example\", repetitions=100)\n_ = cirq.plot_state_histogram(result, plt.subplot())\nplt.show()"
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"source": [
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"# Now we'll send the job to our backend for processing and plot the results.\n",
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"\n",
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"result = service.run(circuit, name=\"Cirq example\", repetitions=100)\n",
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"_ = cirq.plot_state_histogram(result, plt.subplot())\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"source": "## And that's a wrap!\n\nTo continue learning with Cirq, check out more examples and documentation at https://quantumai.google/cirq",
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"metadata": {}
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"metadata": {},
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"source": [
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"## And that's a wrap!\n",
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"\n",
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"To continue learning with Cirq, check out more examples and documentation at https://quantumai.google/cirq"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv (3.14.2)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}

‎cuda-quantum/main.ipynb‎

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},
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{
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"cell_type": "markdown",
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"source": "# CUDA-Q\n\n[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ionq-samples/getting-started/blob/main/cuda-quantum/main.ipynb)\n\n[CUDA-Q](https://nvidia.github.io/cuda-quantum/latest/) is NVIDIA's platform for hybrid quantum-classical computing. It provides a unified programming model for quantum algorithm development and seamlessly integrates with classical computing workflows.",
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"metadata": {}
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"metadata": {},
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"source": [
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"# CUDA-Q\n",
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"\n",
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ionq-samples/getting-started/blob/main/cuda-quantum/main.ipynb)\n",
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"\n",
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"[CUDA-Q](https://nvidia.github.io/cuda-quantum/latest/) is NVIDIA's platform for hybrid quantum-classical computing. It provides a unified programming model for quantum algorithm development and seamlessly integrates with classical computing workflows."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "import os\n\nimport cudaq\n\nfrom helpers import get_ionq_api_key\n\napi_key = get_ionq_api_key()\nos.environ[\"IONQ_API_KEY\"] = api_key # Remove when set_target takes api_key\ncudaq.set_target(\"ionq\", qpu=\"simulator\")"
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"source": [
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"import os\n",
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"\n",
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"import cudaq\n",
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"\n",
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"from helpers import get_ionq_api_key\n",
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"\n",
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"api_key = get_ionq_api_key()\n",
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"os.environ[\"IONQ_API_KEY\"] = api_key # Remove when set_target takes api_key\n",
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"cudaq.set_target(\"ionq\", qpu=\"simulator\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Now we set up our circuit. In this case, we're creating a Bell state with two\n# qubits, applying an H gate to qubit-0, a controlled-X gate to both qubits.\n\nSHOTS = 100\n\n\n@cudaq.kernel\ndef bell_state():\n qubits = cudaq.qvector(2)\n h(qubits[0])\n x.ctrl(qubits[0], qubits[1])\n\n\n# Execute the kernel on IonQ's simulator and display the results.\n\nresult = cudaq.sample(bell_state, shots_count=SHOTS)\nprint(result)"
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"source": [
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"# Now we set up our circuit. In this case, we're creating a Bell state with two\n",
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"# qubits, applying an H gate to qubit-0, a controlled-X gate to both qubits.\n",
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"\n",
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"SHOTS = 100\n",
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"\n",
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"\n",
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"@cudaq.kernel\n",
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"def bell_state():\n",
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" qubits = cudaq.qvector(2)\n",
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" h(qubits[0])\n",
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" x.ctrl(qubits[0], qubits[1])\n",
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"\n",
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"\n",
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"# Execute the kernel on IonQ's simulator and display the results.\n",
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"\n",
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"result = cudaq.sample(bell_state, shots_count=SHOTS)\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"source": "## And that's a wrap!\n\nTo explore more about CUDA-Q and quantum computing with NVIDIA, check out the official documentation at https://nvidia.github.io/cuda-quantum/",
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"metadata": {}
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"metadata": {},
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"source": [
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"## And that's a wrap!\n",
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"\n",
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"To explore more about CUDA-Q and quantum computing with NVIDIA, check out the official documentation at https://nvidia.github.io/cuda-quantum/"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv (3.14.2)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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},
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"orig_nbformat": 4
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

‎pennylane.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"source": [
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "from functools import partial\n\nimport matplotlib.pyplot as plt\nimport pennylane as qml\n\nfrom helpers import get_ionq_api_key\n\n# Before you begin, get your API key from https://cloud.ionq.com/settings/keys\n\n# If your API key is stored as \"IONQ_API_KEY\" in your local environment, this\n# should find it. Otherwise you'll be prompted to enter your API key manually.\n\napi_key = get_ionq_api_key()\n\n# We need to specify the device where the circuit will be executed. In\n# this case we're using the `ionq.simulator`, but if you have QPU access you\n# can specify it here to run the job on a QPU directly.\n\ndev = qml.device(\n 'ionq.simulator',\n api_key=api_key,\n wires=2,\n)"
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"source": [
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"from functools import partial\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import pennylane as qml\n",
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"\n",
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"from helpers import get_ionq_api_key\n",
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"\n",
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"# Before you begin, get your API key from https://cloud.ionq.com/settings/keys\n",
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"\n",
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"# If your API key is stored as \"IONQ_API_KEY\" in your local environment, this\n",
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"# should find it. Otherwise you'll be prompted to enter your API key manually.\n",
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"\n",
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"api_key = get_ionq_api_key()\n",
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"\n",
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"# We need to specify the device where the circuit will be executed. In\n",
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"# this case we're using the `ionq.simulator`, but if you have QPU access you\n",
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"# can specify it here to run the job on a QPU directly.\n",
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"\n",
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"dev = qml.device(\n",
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" 'ionq.simulator',\n",
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" api_key=api_key,\n",
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" wires=2,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Now we set up our circuit. In this case, we're creating a circuit with two\n# qubits, applying an H gate to qubit-0, a CNOT gate to both qubits.\n\n@partial(qml.set_shots, shots=100)\n@qml.qnode(dev)\ndef circuit():\n qml.Hadamard(wires=0)\n qml.CNOT(wires=[0, 1])\n return qml.counts()"
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"source": [
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"# Now we set up our circuit. In this case, we're creating a circuit with two\n",
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"# qubits, applying an H gate to qubit-0, a CNOT gate to both qubits.\n",
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"\n",
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"@partial(qml.set_shots, shots=100)\n",
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"@qml.qnode(dev)\n",
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"def circuit():\n",
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" qml.Hadamard(wires=0)\n",
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" qml.CNOT(wires=[0, 1])\n",
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" return qml.counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Before submitting the job, we can visualize the circuit using draw_mpl().\n\nfig, ax = qml.draw_mpl(circuit, style='sketch')()"
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"source": [
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"# Before submitting the job, we can visualize the circuit using draw_mpl().\n",
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"\n",
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"fig, ax = qml.draw_mpl(circuit, style='sketch')()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Now we'll send the job to our backend for processing and plot the results.\n\ncounts = circuit()\n\n# Robust scalar conversion (works whether values are Python ints, numpy scalars, torch scalars, etc.)\ncounts = {k: int(v) for k, v in counts.items()}\n\n# Optional: If you want missing outcomes shown as 0, define the QNode with counts(all_outcomes=True).\n\nlabels = sorted(counts.keys())\nvalues = [counts[k] for k in labels]\n\nplt.bar(labels, values)\nplt.xlabel(\"bitstring\")\nplt.ylabel(\"counts\")\nplt.title(\"Measurement counts\")\nplt.show()"
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"source": [
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"# Now we'll send the job to our backend for processing and plot the results.\n",
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"\n",
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"counts = circuit()\n",
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"\n",
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"# Robust scalar conversion (works whether values are Python ints, numpy scalars, torch scalars, etc.)\n",
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"counts = {k: int(v) for k, v in counts.items()}\n",
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"\n",
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"# Optional: If you want missing outcomes shown as 0, define the QNode with counts(all_outcomes=True).\n",
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"\n",
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"labels = sorted(counts.keys())\n",
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"values = [counts[k] for k in labels]\n",
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"\n",
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"plt.bar(labels, values)\n",
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"plt.xlabel(\"bitstring\")\n",
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"plt.ylabel(\"counts\")\n",
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"plt.title(\"Measurement counts\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"source": "## And that's a wrap!\n\nTo continue learning with PennyLane, check out more examples and tutorials at https://pennylane.ai/qml/",
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"metadata": {}
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"metadata": {},
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"source": [
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"## And that's a wrap!\n",
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"\n",
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"To continue learning with PennyLane, check out more examples and tutorials at https://pennylane.ai/qml/"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv (3.14.2)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.2"
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}

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