https://github.com/rigetti/quantumflow

QuantumFlow: A Quantum Algorithms Development Toolkit
https://github.com/rigetti/quantumflow

automatic-differentiation quantum-algorithms quantum-computing quantum-machine-learning

Last synced: 3 months ago
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QuantumFlow: A Quantum Algorithms Development Toolkit

• Host: GitHub
• URL: https://github.com/rigetti/quantumflow
• Owner: rigetti
• License: apache-2.0
• Created: 2018-11-01T17:04:32.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-06-03T03:20:31.000Z (over 5 years ago)
• Last Synced: 2024-02-15T12:32:56.644Z (11 months ago)
• Topics: automatic-differentiation, quantum-algorithms, quantum-computing, quantum-machine-learning
• Language: Python
• Homepage: https://quantumflow.readthedocs.io/
• Size: 403 KB
• Stars: 95
• Watchers: 18
• Forks: 24
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
>Notice: This is research code that will not necessarily be maintained to

>support further releases of Forest and other Rigetti Software. We welcome

>bug reports and PRs but make no guarantee about fixes or responses.

# QuantumFlow: A Quantum Algorithms Development Toolkit

[![Build Status](https://travis-ci.org/rigetti/quantumflow.svg?branch=master)](https://travis-ci.org/rigetti/quantumflow)

## Installation for development

It is easiest to install QuantumFlow's requirements using conda.

```

git clone https://github.com/rigetti/quantumflow.git

cd quantumflow

conda install -c conda-forge --file requirements.txt

pip install -e .

```

You can also install with pip. However some of the requirements are tricky to install (notably tensorflow & cvxpy), and (probably) not everything in QuantumFlow will work correctly.

```

git clone https://github.com/rigetti/quantumflow.git

cd quantumflow

pip install -r requirements.txt

pip install -e .

```

## Example

Train the QAOA algorithm, with back-propagation gradient descent, to perform

MAXCUT on a randomly chosen 6 node graph. 

```bash

./examples/qaoa_maxcut.py --verbose --steps 5 --nodes 6 random

```