A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography

TL;DR

This paper introduces an unsupervised machine learning framework combining classical shadow tomography and PCA to identify and classify diverse quantum phase transitions without prior knowledge of the system.

Contribution

It presents a general, data-driven methodology that detects and distinguishes quantum phase transitions using statistical patterns from classical shadow data, applicable to various models.

Findings

Successfully detects symmetry-breaking and topological transitions.

Effectively classifies phase transitions based on fluctuation patterns.

Works across multiple spin systems without prior Hamiltonian knowledge.

Abstract

We provide a general machine learning methodology that integrates classical shadow representations with unsupervised principal component analysis (PCA) to explore various quantum phase transitions. By sampling spin configurations from random Pauli measurements, our approach can effectively analyze hidden statistical patterns in the data, thereby capturing the distinct signatures of quantum criticality through their fluctuations. We benchmark this approach across various spin-1/2 systems, including the 1D XZX cluster-Ising model, the 1D bond-alternating XXZ model, the 2D transverse-field Ising model, and the 2D Kitaev honeycomb model. We show that PCA not only reliably detects and distinguishes both symmetry-breaking and topological transitions, but also enables their qualitative classification based on characteristic fluctuation patterns. Our data-driven approach does not require any knowledge of the Hamiltonian or explicit order parameters, and can therefore be a general and applicable tool for probing new quantum phases.