Experimentally modeling stochastic processes with less memory by the use of a quantum processor
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 inform...
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sg-ntu-dr.10356-901682023-02-28T19:36:49Z Experimentally modeling stochastic processes with less memory by the use of a quantum processor Palsson, Matthew S. Gu, Mile Ho, Joseph Wiseman, Howard M. Pryde, Geoff J. School of Physical and Mathematical Sciences Complexity Institute Quantum Information Complexity DRNTU::Science::Chemistry 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. Published version 2018-12-26T02:48:24Z 2019-12-06T17:42:15Z 2018-12-26T02:48:24Z 2019-12-06T17:42:15Z 2017 Journal Article 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 https://hdl.handle.net/10356/90168 http://hdl.handle.net/10220/47192 10.1126/sciadv.1601302 en Science Advances © 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). 6 p. application/pdf |
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Quantum Information Complexity DRNTU::Science::Chemistry Palsson, Matthew S. Gu, Mile Ho, Joseph Wiseman, Howard M. Pryde, Geoff J. Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Palsson, Matthew S. Gu, Mile Ho, Joseph Wiseman, Howard M. Pryde, Geoff J. |
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Article |
author |
Palsson, Matthew S. Gu, Mile Ho, Joseph Wiseman, Howard M. Pryde, Geoff J. |
author_sort |
Palsson, Matthew S. |
title |
Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
title_short |
Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
title_full |
Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
title_fullStr |
Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
title_full_unstemmed |
Experimentally modeling stochastic processes with less memory by the use of a quantum processor |
title_sort |
experimentally modeling stochastic processes with less memory by the use of a quantum processor |
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2018 |
url |
https://hdl.handle.net/10356/90168 http://hdl.handle.net/10220/47192 |
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1759857228394790912 |