Unsupervised interpretable learning of phases from many-qubit systems

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...

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Published inPhysical review research Vol. 5; no. 1; p. 013082
Main Authors Sadoune, Nicolas, Giudici, Giuliano, Liu, Ke, Pollet, Lode
Format Journal Article
LanguageEnglish
Published American Physical Society 01.02.2023
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ISSN2643-1564
2643-1564
DOI10.1103/PhysRevResearch.5.013082

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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.
AbstractList 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.
ArticleNumber 013082
Author Sadoune, Nicolas
Pollet, Lode
Liu, Ke
Giudici, Giuliano
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CitedBy_id crossref_primary_10_1103_PhysRevB_108_205152
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crossref_primary_10_1103_PhysRevB_110_214415
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Title Unsupervised interpretable learning of phases from many-qubit systems
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