Neural-Network Decoders for Measurement Induced Phase Transitions

TL;DR

This paper introduces a neural network decoder approach to detect measurement-induced entanglement phase transitions in quantum systems, offering a scalable alternative to traditional methods by analyzing the learnability of the decoder function.

Contribution

It proposes using neural networks to identify entanglement phase transitions through local measurements, improving feasibility and scalability over previous exponential repetition methods.

Findings

Decoder learnability sharply changes at phase transition

Neural network approach is scalable to larger systems

Method can be applied to generic quantum experiments

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.