PIRSA:19070024

Video URL

https://pirsa.org/19070024

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

van Nieuwenburg, E. (2019). Integrating Neural Networks with a Quantum Simulator for State Reconstruction. Perimeter Institute for Theoretical Physics. https://pirsa.org/19070024

van Nieuwenburg, Evert. Integrating Neural Networks with a Quantum Simulator for State Reconstruction. Perimeter Institute for Theoretical Physics, Jul. 09, 2019, https://pirsa.org/19070024 

          @misc{ scivideos_PIRSA:19070024,
            doi = {10.48660/19070024},
            url = {https://pirsa.org/19070024},
            author = {van Nieuwenburg, Evert},
            keywords = {Quantum Matter},
            language = {en},
            title = {Integrating Neural Networks with a Quantum Simulator for State Reconstruction},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {jul},
            note = {PIRSA:19070024 see, \url{https://scivideos.org/index.php/pirsa/19070024}}
          }

Evert van Nieuwenburg Leiden University

Talk numberPIRSA:19070024
DOI10.48660/19070024
Source RepositoryPIRSA
Collection
Talk Type Conference
Subject

Abstract

In this talk I will discuss how (unsupervised) machine learning methods can be useful for quantum experiments. Specifically, we will consider the use of a generative model to perform quantum many-body (pure) state reconstruction directly from experimental data. The power of this machine learning approach enables us to trade few experimentally complex measurements for many simpler ones, allowing for the extraction of sophisticated observables such as the Rényi mutual information. These results open the door to integration of machine learning architectures with intermediate-scale quantum hardware.