Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer

TL;DR

This paper introduces a variational quantum anomaly detection method that unsupervisedly maps phase diagrams of quantum systems directly on quantum hardware, enabling efficient analysis of complex quantum data without prior knowledge.

Contribution

It presents a novel unsupervised quantum machine learning algorithm that extracts phase diagrams directly on quantum computers, including real-device implementation.

Findings

Successfully mapped the phase diagram of a 1D extended Bose Hubbard model.

Demonstrated the algorithm's feasibility on current quantum hardware.

Identified symmetry protected topological phases using the method.

Abstract

One of the most promising applications of quantum computing is simulating quantum many-body systems. However, there is still a need for methods to efficiently investigate these systems in a native way, capturing their full complexity. Here, we propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation. The algorithm is used to extract the phase diagram of a system with no prior physical knowledge and can be performed end-to-end on the same quantum device that the system is simulated on. We showcase its capabilities by mapping out the phase diagram of the one-dimensional extended Bose Hubbard model with dimerized hoppings, which exhibits a symmetry protected topological phase. Further, we show that it can be used with readily accessible devices nowadays and perform the algorithm on a real quantum computer.