Quantum data learning for quantum simulations in high-energy physics

TL;DR

This paper investigates the use of quantum-data learning with quantum neural networks to identify phases and parameters in high-energy physics models, demonstrating promising results in recognizing quantum states and extracting physical constants.

Contribution

It introduces a quantum convolutional neural network approach for quantum-data learning applied to high-energy physics, showing its effectiveness in recognizing phases and extracting parameters from quantum states.

Findings

Successfully recognized quantum phases in the Schwinger model

Identified (de)confinement phases in $ ext{Z}_2$ gauge theory

Extracted fermion flavor and coupling constants

Abstract

Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages over classical counterparts in practical learning tasks is yet to be demonstrated. A promising avenue to explore potential advantages is the learning of data generated by quantum mechanical systems and presented in an inherently quantum mechanical form. In this article, we explore the applicability of quantum-data learning to practical problems in high-energy physics, aiming to identify domain specific use-cases where quantum models can be employed. We consider quantum states governed by one-dimensional lattice gauge theories and a phenomenological quantum field theory in particle physics, generated by digital quantum simulations or variational methods to approximate target states. We make use of an ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states in the Schwinger model, (de)confinement phases from time-evolved states in the $\mathbb{Z}_2$ gauge theory, and that it can extract fermion flavor/coupling constants in a quantum simulation of parton shower. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.