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Proceedings Paper

Quantum devices for memory reduction (Conference Presentation)
Author(s): Nora Tischler; Farzad Ghafari; Alex Pepper; Carlo Di Franco; Jayne Thompson; Mile Gu; Howard M. Wiseman; Geoff J. Pryde

Paper 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.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology, 112950H (10 March 2020); doi: 10.1117/12.2548908
Show Author Affiliations
Nora Tischler, Griffith Univ. (Australia)
Farzad Ghafari, Griffith Univ. (Australia)
Alex Pepper, Griffith Univ. (Australia)
Carlo Di Franco, Nanyang Technological Univ. (Singapore)
Jayne Thompson, National Univ. of Singapore (Singapore)
Mile Gu, Nanyang Technological Univ. (Singapore)
Howard M. Wiseman, Griffith Univ. (Australia)
Geoff J. Pryde, Griffith Univ. (Australia)


Published in SPIE Proceedings Vol. 11295:
Advanced Optical Techniques for Quantum Information, Sensing, and Metrology
Philip R. Hemmer; Alan L. Migdall; Zameer Ul Hasan, Editor(s)

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