Unsupervised Interpretable Learning of Phases From Many-Qubit Systems

Abstract

Experimental progress in qubit manufacturing calls for the development of new theoretical tools to analyze quantum data. We show how an unsupervised machine-learning technique can be used to understand short-range entangled many-qubit systems using data of local measurements. The method successfully constructs the phase diagram of a cluster-state model and detects the respective order parameters of its phases, including string order parameters. For the toric code subject to external magnetic fields, the machine identifies the explicit forms of its two stabilizers. Prior information of the underlying Hamiltonian or the quantum states is not needed; instead, the machine outputs their characteristic observables. Our work opens the door for a first-principles application of hybrid algorithms that aim at strong interpretability without supervision.

9 pages, 7 figures

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• The density-matrix renormalization group in the age of matrix product states
• Efficient quantum state tomography
• Valence Bond Solids for Quantum Computation
• Persistent Homology of ${\mathbb{Z}}_2$ Gauge Theories

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• Artificial intelligence for representing and characterizing quantum systems
• A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography