Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87218
Title: Provably unbounded memory advantage in stochastic simulation using quantum mechanics
Authors: Garner, Andrew J. P.
Liu, Qing
Thompson, Jayne
Vedral, Vlatko
Gu, Mile
Keywords: Quantum Advantage
Quantum Information
Issue Date: 2017
Source: Garner, A. J. P., Liu, Q., Thompson, J., Vedral, V., & Gu, M. (2017). Provably unbounded memory advantage in stochastic simulation using quantum mechanics. New Journal of Physics, 19(10), 103009-.
Series/Report no.: New Journal of Physics
Abstract: Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart.
URI: https://hdl.handle.net/10356/87218
http://hdl.handle.net/10220/44337
DOI: 10.1088/1367-2630/aa82df
Rights: © 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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