Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104750
Title: Quantum reservoir processing
Authors: Ghosh, Sanjib
Opala, Andrzej
Matuszewski, Michał
Paterek, Tomasz
Liew, Timothy C. H.
Keywords: DRNTU::Science::Physics
Quantum Neural Network
Quantum Information Processing
Issue Date: 2019
Source: Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. H. (2019). Quantum reservoir processing. npj Quantum Information, 5(1). doi:10.1038/s41534-019-0149-8
Series/Report no.: npj Quantum Information
Abstract: The concurrent rise of artificial intelligence and quantum information poses an opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of an artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a nonlinear function of an input quantum state (e.g., entropy, purity, or logarithmic negativity). In this way, experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.
URI: https://hdl.handle.net/10356/104750
http://hdl.handle.net/10220/48758
DOI: 10.1038/s41534-019-0149-8
DOI (Related Dataset): https://doi.org/10.21979/N9/VIMXR5
Rights: © The Author(s) 2019. 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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