update links for colab (#48)

* update links for colab

* test common versions of python

* fix indentation

* lol requests not requirements

* pin ubuntu version

* test specifying the architecture

* test to remove python3.12

* pip freeze to help pip

* oh good luck

* maybe 3.9 cant resolve

* test old deps with no dep checking

* upgrade pip

* dont specify versions

* revert requirements and actions

* try using the conda package manager

* remove pip requirements

* attempt to fix conda init error

* remove existing conda envs

* add pip dependency to conda

* pls work conda pls

.github/workflows/test_notebooks.yml

@@ -1,24 +1,43 @@
 name: Test Notebooks
 
-on: push
+on:
+ push:
+ branches:
+ - main
+ pull_request:
+ branches:
+ - main
 
 jobs:
  test:
  runs-on: ubuntu-22.04
  steps:
- - name: Check out code
- uses: actions/checkout@v3
+  - name: Check out code
+  uses: actions/checkout@v4
 
- - name: Set up Python
- uses: actions/setup-python@v4
- with:
- python-version: '3.11'
+ - name: Set up Conda
+ uses: conda-incubator/setup-miniconda@v2
+ with:
+ activate-environment: myenv
+ environment-file: environment.yml
+ python-version: "3.11"
+ auto-activate-base: false
 
- - name: Install dependencies
- run: |
- pip install -r requirements.txt
+ - name: Prepare Conda environment
+ shell: bash -l {0}
+ run: |
+ if conda env list | grep -q 'myenv'; then
+ echo "Environment 'myenv' already exists, updating environment"
+ conda env update --name myenv --file environment.yml --prune
+ else
+ echo "Creating new environment 'myenv'"
+ conda env create -f environment.yml
+ fi
 
- - name: Run notebook tests
- run: python tests/test_notebooks.py
- env:
- IONQ_API_KEY: ${{ secrets.IONQ_API_KEY }}
+ - name: Run notebook tests
+ shell: bash -l {0}
+ run: |
+ conda activate myenv
+ python tests/test_notebooks.py
+ env:
+ IONQ_API_KEY: ${{ secrets.IONQ_API_KEY }}

.gitignore

@@ -28,6 +28,7 @@ share/python-wheels/
 # Distribution / packaging
 .Python
 env/
+venv/
 ionq/
 build/
 develop-eggs/

README.md

@@ -4,18 +4,22 @@ This repository contains Python samples exploring quantum computing on IonQ's pl
 
 If you're looking for advanced and in-depth examples for a given library that implement a specific algorithm, check out some of the other projects in the [ionq-samples](https://github.com/ionq-samples) organization on GitHub.
 
-----
+---
 
 ## Prerequisites
 
 There are a wide variety of ways to run these notebooks, but for starters you'll need:
 
 1. [Python](https://www.python.org/downloads/) installed, using a version between 3.8 and 3.11.
-1. A [virtual environment](https://docs.python.org/3/library/venv.html) to help ensure your dependencies don't conflict with anything else you have installed.
-1. An [IonQ API key](https://cloud.ionq.com/settings/keys), which optionally you can store as an environment variable for ease of use. Our notebooks expect to find it stored as `IONQ_API_KEY`.
-1. An installation of the library you're wanting to run. To install all the libraries at once, run `pip install -r requirements.txt` from the root directory of this repository.
+2. A [virtual environment](https://docs.python.org/3/library/venv.html) to help ensure your dependencies don't conflict with anything else you have installed.
+3. An [IonQ API key](https://cloud.ionq.com/settings/keys), which optionally you can store as an environment variable for ease of use. Our notebooks expect to find it stored as `IONQ_API_KEY`.
+4. An installation of the library you're wanting to run. To install all the libraries at once using Conda, run the following command from the root directory of this repository:
 
-----
+ ```shell
+ conda env create -f environment.yml
+ ```
+
+---
 
 ## Usage
 
@@ -27,21 +31,24 @@ If you're unfamiliar with Jupyter but you're used to a traditional IDE or code e
 
 1. From your terminal, navigate to this repository and run the following command from within this directory:
 
- ```shell
- jupyter notebook
- ```
+ ```shell
+ jupyter notebook
+ ```
+
 1. Once the server is started, it should automatically open your browser. In case it doesn't, you can navigate directly to it by pointing your browser at [http://localhost:8888](http://localhost:8888)
 1. Navigate to the location of a `.ipynb` file and open it. If you don't have a particular SDK in mind, we recommend starting with `qiskit`, as its the most commonly used library today.
 
 #### VS Code
+
 1. Open the folder in VS Code and navigate to a `.ipynb` file and open it.
 1. If it's your first time using it, it will suggest a number of plugins that you may need to install before the notebook will be fully functional.
 1. At the top-right of the screen, click on `Select Kernel` and choose an appropriate Python runtime to run the notebook in.
 
 #### Cloud
+
 1. Open the notebook by clicking on the ![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg) badge located in each notebook. Or open this repository in [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ionq-samples/getting-started/HEAD)
 
-----
+---
 
 ## Support
 

api.ipynb

@@ -6,7 +6,7 @@
  "source": [
  "# Direct API Access\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/api/main.ipynb)\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/api.ipynb)\n",
  "\n",
  "IonQ offers a set of API resources that allow you to submit and manage jobs directly. This notebook offers a few simple examples, but visit [docs.ionq.com](https://docs.ionq.com) for a complete guide with additional resources and functionality."
  ]
@@ -176,7 +176,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.11.8"
  },
  "orig_nbformat": 4
  },

cirq.ipynb

@@ -6,9 +6,9 @@
  "source": [
  "# Cirq\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/cirq/main.ipynb)\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/cirq.ipynb)\n",
  "\n",
- "[Cirq](https://github.com/quantumlib/Cirq) is an open-source Python library developed by Google for designing, simulating, and running quantum circuits on quantum computers."
+ "[Cirq](https://github.com/quantumlib/Cirq) is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. It is designed to be flexible, and is suitable for use in a variety of quantum computing tasks."
  ]
  },
  {
@@ -122,7 +122,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.11.0"
+ "version": "3.11.8"
  }
  },
  "nbformat": 4,

environment.yml

@@ -0,0 +1,22 @@
+name: myenv
+channels:
+ - conda-forge
+ - defaults
+dependencies:
+ - python=3.11
+ - pip
+ - matplotlib
+ - jupyter
+ # Add other dependencies here, for Conda packages.
+ - pip:
+ # Pip-only packages here, if any.
+ - qiskit
+ - qiskit-ionq
+ - cirq
+ - cirq-ionq
+ - pennylane
+ - pennylane-ionq
+ - projectq
+ - cuda-quantum
+ - pylatexenc
+ - requests

pennylane.ipynb

@@ -6,7 +6,9 @@
  "source": [
  "# PennyLane\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/pennylane/main.ipynb)"
+ "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ionq-samples/getting-started/blob/main/pennylane.ipynb)\n",
+ "\n",
+ "[PennyLane](https://pennylane.ai/) is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. It is designed to work with existing quantum computing hardware and simulators, and integrates seamlessly with machine learning libraries such as TensorFlow and PyTorch."
  ]
  },
  {
@@ -121,7 +123,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.11.8"
  }
  },
  "nbformat": 4,

projectq.ipynb

@@ -6,9 +6,9 @@
  "source": [
  "# ProjectQ\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/projectq/main.ipynb)\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/projectq.ipynb)\n",
  "\n",
- "ProjectQ is an open-source software framework for quantum computing that allows users to design, simulate, and optimize quantum circuits on various backends."
+ "[ProjectQ](https://projectq.ch/) is an open-source software framework for quantum computing that allows users to design, simulate, and optimize quantum circuits on various backends."
  ]
  },
  {

qiskit.ipynb

@@ -7,7 +7,7 @@
  "source": [
  "# Getting Started with Qiskit\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/qiskit/main.ipynb)\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/qiskit.ipynb)\n",
  "\n",
  "[Qiskit](https://qiskit.org/) is an open-source quantum computing framework developed by IBM for building, simulating, and running quantum circuits on real and simulated quantum devices."
  ]