An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Owen Lockwood

Last update: Jun 28, 2022

Related tags

Deep Learning Model Explanation qvc_opt_review

Overview

QVC Optimizer Review

Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits".

Each of the python files can be run and will generate the results for the associated set of problem setups. To enable/disable noise, comment out or uncomment the only line that has the .with_noise(cirq.depolarize(p=0.01)).

All the results are available in the results.txt organized by task, score, and standard deviation over the 3 runs.

Open Call For Research

Check out the CONTRIBUTING.md and take on one of the issues (or make your own). For more information, see the video:

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Comments

Feature Request: NFT optimizer

I think it would be great to include the NFT optimizer to these benchmarks. I believe implementations are mainly dependent on optimizers that are already implemented in Tensorflow, therefore it might require some work. Qiskit has an implementation that is based on scipy, maybe that can be leveraged in some way.

opened by cnktysz 2
QAOA Problems

Currently, the QAOA is only applied to the MAX-CUT problem. However, there are a number of different important combinatorial optimization problems that QAOA have been applied to. Pick one (or more) of these problems and generate new test cases and results.

opened by lockwo 0

Owner

Owen Lockwood

GitHub

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An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Related tags

Overview

QVC Optimizer Review

Open Call For Research

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