Quantum Phase Classification of Rydberg Atom Systems Using Resource-Efficient Variational Quantum Circuits and Classical Shadows

TL;DR

This paper introduces a resource-efficient quantum machine learning method combining classical shadows and variational quantum circuits to accurately classify quantum phases in Rydberg atom systems, suitable for near-term quantum devices.

Contribution

It presents a novel pipeline that uses classical shadow tomography with minimal quantum resources for high-accuracy phase classification in many-body physics.

Findings

Achieved 100% test accuracy in phase classification.

Used only 2 qubits for encoding, demonstrating resource efficiency.

Converged training in 120 iterations with SPSA.

Abstract

Quantum phase transitions in Rydberg atom arrays present significant opportunities for studying many-body physics, yet distinguishing between different ordered phases without explicit order parameters remains challenging. We present a resource-efficient quantum machine learning approach combining classical shadow tomography with variational quantum circuits (VQCs) for binary phase classification of Z2 and Z3 ordered phases. Our pipeline processes 500 randomized measurements per 51-atom chain state, reconstructs shadow operators, performs PCA dimensionality reduction (514 features), and encodes features using angle embedding onto a 2-qubit parameterized circuit. The circuit employs RY-RZ angle encoding, strong entanglement via all-to-all CZ gates, and a minimal 2-parameter ansatz achieving depth 7. Training via simultaneous perturbation stochastic approximation (SPSA) with hinge loss converged in 120 iterations. The model achieved 100% test accuracy with perfect precision, recall, and F1 scores, demonstrating that minimal quantum resources suffice for high-accuracy phase classification. This work establishes pathways for quantum-enhanced condensed matter physics on near-term quantum devices.