PIRSA: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. https://pirsa.org/19070024

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

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

Evert van Nieuwenburg

Leiden University

Talk number
PIRSA:19070024
Collection
Talk Type
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.