Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection

TL;DR

This paper introduces a hybrid classical-quantum approach using AI techniques to learn quantum circuit parameters and detect quantum phase transitions without direct measurement of physical observables.

Contribution

It presents a novel framework combining LLM attention mechanisms with a variational autoencoder to efficiently learn and analyze quantum states and phase transitions.

Findings

Successfully detects topological quantum phase transitions

Efficiently captures hidden correlations in quantum circuit parameters

Broad applicability across different quantum systems

Abstract

Learning many-body quantum states and quantum phase transitions remains a major challenge in quantum many-body physics. Classical machine learning methods offer certain advantages in addressing these difficulties. In this work, we propose a novel framework that bypasses the need to measure physical observables by directly learning the parameters of parameterized quantum circuits. By integrating the attention mechanism from large language models (LLMs) with a variational autoencoder (VAE), we efficiently capture hidden correlations within the circuit parameters. These correlations allow us to extract information about quantum phase transitions in an unsupervised manner. Moreover, our VAE acts as a classical representation of parameterized quantum circuits and the corresponding many-body quantum states, enabling the efficient generation of quantum states associated with specific phases. We apply our framework to a variety of quantum systems and demonstrate its broad applicability, with particularly strong performance in identifying topological quantum phase transitions.