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Neural-network decoders for measurement induced phase transitions

Published

Author(s)

Hossein Dehghani, Ali Lavasani, Mohammad Hafezi, Michael Gullans

Abstract

Open quantum systems have been shown to host a plethora of exotic dynamical phases. Measurement-induced entanglement phase transitions in monitored quantum systems are a striking example of this phenomena. However, naive realizations of such phase transitions requires an exponential number of repetitions of the experiment which is practically unfeasible on large systems. Recently, it has been proposed that these phase transitions can be probed locally via entangling reference qubits and studying their purification dynamics. In this work, we leverage modern machine learning tools to devise a neural network decoder to determine the state of the reference qubits conditioned on the measurement outcomes. We show that the entanglement phase transition manifests itself as a stark change in the learnability of the decoder function. We study the complexity and scalability of this approach and discuss how it can be utilized to detect entanglement phase transitions in generic experiments.
Citation
Nature Communications
Volume
14
Issue
1

Keywords

Machine learning, Quantum Error Correction, Phase transitions

Citation

Dehghani, H. , Lavasani, A. , Hafezi, M. and Gullans, M. (2023), Neural-network decoders for measurement induced phase transitions, Nature Communications, [online], https://doi.org/10.1038/s41467-023-37902-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934964 (Accessed June 22, 2024)

Issues

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Created May 22, 2023, Updated July 20, 2023