Quantum devices for memory reduction (Conference Presentation)

Nora Tischler; Farzad Ghafari; Alex Pepper; Carlo Di Franco; Jayne Thompson; Mile Gu; Howard M. Wiseman; Geoff J. Pryde

doi:10.1117/12.2548908

10 March 2020 Quantum devices for memory reduction (Conference Presentation)

Nora Tischler, Farzad Ghafari, Alex Pepper, Carlo Di Franco, Jayne Thompson, Mile Gu, Howard M. Wiseman, Geoff J. Pryde

Proceedings Volume 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology; 112950H (2020) https://doi.org/10.1117/12.2548908
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

Memory is a precious commodity across many different areas. I will present two ways in which quantum devices can lower memory requirements. The first application is the simulation of stochastic processes, i.e. processes that exhibit some randomness. We have experimentally realized quantum simulations of classical stochastic processes. Our simulators have lower memory requirements than the optimal classical simulators. The second type of quantum device I will discuss is a quantum autoencoder, which autonomously learns how to compress quantum data. We have developed and experimentally realized a photonic quantum autoencoder that is trained based on sets of quantum states.

Conference Presentation

Citation Download Citation

Nora Tischler, Farzad Ghafari, Alex Pepper, Carlo Di Franco, Jayne Thompson, Mile Gu, Howard M. Wiseman, and Geoff J. Pryde "Quantum devices for memory reduction (Conference Presentation)", Proc. SPIE 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology, 112950H (10 March 2020); https://doi.org/10.1117/12.2548908

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