PIRSA:22040010

Video URL

https://pirsa.org/22040010

Neural Network Decoders for Measurement-Induced Phase Transitions

Gullans, M. (2022). Neural Network Decoders for Measurement-Induced Phase Transitions. Perimeter Institute for Theoretical Physics. https://pirsa.org/22040010

Gullans, Michael. Neural Network Decoders for Measurement-Induced Phase Transitions. Perimeter Institute for Theoretical Physics, Apr. 27, 2022, https://pirsa.org/22040010 

          @misc{ scivideos_PIRSA:22040010,
            doi = {10.48660/22040010},
            url = {https://pirsa.org/22040010},
            author = {Gullans, Michael},
            keywords = {Quantum Information},
            language = {en},
            title = {Neural Network Decoders for Measurement-Induced Phase Transitions},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {apr},
            note = {PIRSA:22040010 see, \url{https://scivideos.org/index.php/pirsa/22040010}}
          }

Michael Gullans University of Maryland, College Park

Talk numberPIRSA:22040010
DOI10.48660/22040010
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

The sustained storage, transmission, or processing of quantum information will likely be a non-equilibrium process that requires monitoring the system and applying some form of feedback to produce fault-tolerance.  In this talk, I will discuss a class of models based on random quantum circuits with intermediate measurements that display a similar phenomenology to standard models for fault-tolerance, including the existence of a threshold, but with several helpful simplifications.  However, naïve realizations of the threshold require an exponential number of repetitions of the experiment to fully explore the output space of the intermediate measurements.  Recently, it has been proposed that this problem can be circumvented by developing efficient entanglement “decoders” that have close parallels to quantum error correction decoders.  We show how to leverage modern machine learning tools to devise a neural network decoder to detect the phase transition. We then study the complexity and scalability of this approach and discuss how it can be utilized to detect entanglement phase transitions in generic experiments.

Zoom Link: https://pitp.zoom.us/j/99123641139?pwd=VmkyR3BSNWF5bURVYmFVakp0ZkNRZz09