Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152951
Title: Realising and compressing quantum circuits with quantum reservoir computing
Authors: Ghosh, Sanjib
Krisnanda, Tanjung
Paterek, Tomasz
Liew, Timothy Chi Hin
Keywords: Science::Physics
Issue Date: 2021
Source: Ghosh, S., Krisnanda, T., Paterek, T. & Liew, T. C. H. (2021). Realising and compressing quantum circuits with quantum reservoir computing. Communications Physics, 4(1), 105-. https://dx.doi.org/10.1038/s42005-021-00606-3
Project: MOE2019-T2-1-004
Journal: Communications Physics
Abstract: Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.
URI: https://hdl.handle.net/10356/152951
ISSN: 2399-3650
DOI: 10.1038/s42005-021-00606-3
Rights: © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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