An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Last update: Jun 28, 2022

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:

Owner

Owen Lockwood

GitHub Repository

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An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Related tags

Overview

QVC Optimizer Review

Open Call For Research

Owner

Owen Lockwood

A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

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L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

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