Neural-Network Decoders for Measurement Induced Phase Transitions
- URL: http://arxiv.org/abs/2204.10904v2
- Date: Mon, 31 Oct 2022 20:52:01 GMT
- Title: Neural-Network Decoders for Measurement Induced Phase Transitions
- Authors: Hossein Dehghani, Ali Lavasani, Mohammad Hafezi, Michael J. Gullans
- Abstract summary: Measurement-induced entanglement phase transitions in monitored quantum systems are a striking example.
We propose 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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