Please use this identifier to cite or link to this item:
Title: Using quantum theory to simplify input–output processes
Authors: Thompson, Jayne
Garner, Andrew J. P.
Vedral, Vlatko
Gu, Mile
Keywords: Quantum Mechanics
Information Theory and Computation
Issue Date: 2017
Source: Thompson, J., Garner, A. J. P., Vedral, V., & Gu, M. (2017). Using quantum theory to simplify input–output processes. npj Quantum Information, 3(1) 6-.
Series/Report no.: npj Quantum Information
Abstract: All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems—algorithmic abstractions of their input–output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency—storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input–output processes depends fundamentally on what sort of information theory we use to describe them.
DOI: 10.1038/s41534-016-0001-3
Rights: © 2017 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

Files in This Item:
File Description SizeFormat 
Using quantum theory to simplify input–output processes.pdf1.03 MBAdobe PDFThumbnail


Updated on Sep 6, 2020


Updated on Jan 14, 2021

Page view(s)

Updated on Jan 16, 2021

Download(s) 50

Updated on Jan 16, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.