Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90168
Title: Experimentally modeling stochastic processes with less memory by the use of a quantum processor
Authors: Palsson, Matthew S.
Gu, Mile
Ho, Joseph
Wiseman, Howard M.
Pryde, Geoff J.
Keywords: Quantum Information
Complexity
DRNTU::Science::Chemistry
Issue Date: 2017
Source: Palsson, M. S., Gu, M., Ho, J., Wiseman, H. M., & Pryde, G. J. (2017). Experimentally modeling stochastic processes with less memory by the use of a quantum processor. Science Advances, 3(2), e1601302-. doi:10.1126/sciadv.1601302
Series/Report no.: Science Advances
Abstract: Computer simulation of observable phenomena is an indispensable tool for engineering new technology, understanding the natural world, and studying human society. However, the most interesting systems are often so complex that simulating their future behavior demands storing immense amounts of information regarding how they have behaved in the past. For increasingly complex systems, simulation becomes increasingly difficult and is ultimately constrained by resources such as computer memory. Recent theoretical work shows that quantum theory can reduce this memory requirement beyond ultimate classical limits, as measured by a process’ statistical complexity, C. We experimentally demonstrate this quantum advantage in simulating stochastic processes. Our quantum implementation observes a memory requirement of Cq = 0.05 ± 0.01, far below the ultimate classical limit of C = 1. Scaling up this technique would substantially reduce the memory required in simulations of more complex systems.
URI: https://hdl.handle.net/10356/90168
http://hdl.handle.net/10220/47192
DOI: http://dx.doi.org/10.1126/sciadv.1601302
Rights: © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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