Evan Garber

Exploration of Universal Quantum Circuits Using Machine Learning

Started Aug. 10, 2023

Abstract or project description

Quantum computers can perform important calculations which are intractable on classical computers, but the size of these calculations is limited by device noise. Techniques from machine learning can be leveraged to automatically discover high-fidelity approximations to quantum circuits that require significantly fewer physical resources. This paper demonstrates the gradient-based discovery of quantum circuits and compares the efficacy of different tunable circuit architectures. We explore the ability of different circuits to implement GHZ state preparation and the quantum Fourier transform to within certain fidelity thresholds as well as optimize the ratio of tunable unitary to fixed controlled gates within these circuits.