Unsupervised Quantum Circuit Learning in High Energy Physics

TL;DR

This paper explores the use of quantum circuit-based generative models, specifically quantum circuit Born machines, for unsupervised data generation in high energy physics, demonstrating their potential in scientific computing.

Contribution

It introduces a non-adversarial, gradient-based training method for quantum circuit Born machines to generate complex joint distributions in high energy physics.

Findings

Quantum circuit Born machines can generate joint distributions over multiple variables.

Gradient-based training effectively trains quantum generative models.

Potential applications in scientific data simulation and analysis.

Abstract

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.