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-QAOA: Optimize QAOA circuits for graph maxcut using tensorflow
TensorFlow open source implementation for training Quantum Approximate Optimization Algorithm (QAOA) circuits on the graph MaxCut problem, from the paper:
Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem
by Gavin E. Crooks
Contact
Code author: Gavin E. Crooks
Pull requests and issues: @gecrooks
Installation
This code relies upon QuantumFlow: A Quantum Algorithms Development Toolkit
git clone https://github.com/rigetticomputing/quantumflow-qaoa.git
cd quantumflow-qaoa
pip install -r requirements.txt
train_qaoa_maxcut_sgd.py
Train a QAOA circuit of N qubits and P steps to find good solutions to the MaxCut problem. We train on randomly sampled graphs, and validate against a fixed set of pregenerated graphs provided by qauntumflow.
> ./train_qaoa_maxcut_sgd.py --help
usage: train_qaoa_maxcut_sgd.py [-h] [--version] [-v] [-i FILE] [-o FILE]
[-N NODES] [-P STEPS] [--epochs EPOCHS]
[--lr LEARNING_RATE] [-T FILE] [-V FILE]
QAOA graph maxcut using tensorflow gradient descent
optional arguments:
-h, --help show this help message and exit
--version show program's version number and exit
-v, --verbose
-i FILE, --fin FILE Read model from file
-o FILE, --fout FILE Write model to file
-N NODES, --nodes NODES
-P STEPS, --steps STEPS
--epochs EPOCHS
--lr LEARNING_RATE
-T FILE, --train FILE
Collection of graphs to train on
-V FILE, --validation FILE
Validation graph dataset
E.g. train 10 epoces on a batch of 100 8 node graphs, with 12 QAOA steps.
./train_qaoa_maxcut_sgd.py -N 8 -P 12 --verbose --epochs 10
Citation
If you use this code, please cite our paper:
@article{Crooks2018b,
title={Performance of the Quantum Approximate Optimization Algorithm
on the Maximum Cut Problem},
author={Crooks, Gavin E},
note={https://arxiv.org/abs/1811.08419},
year={2018}
}
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