Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152419
Title: Reconstructing quantum states with quantum reservoir networks
Authors: Ghosh, Sanjib
Opala, Andrzej
Matuszewski, Michal
Paterek, Tomasz
Liew, Timothy Chi Hin
Keywords: Science::Physics
Issue Date: 2020
Source: Ghosh, S., Opala, A., Matuszewski, M., Paterek, T. & Liew, T. C. H. (2020). Reconstructing quantum states with quantum reservoir networks. IEEE Transactions On Neural Networks and Learning Systems, 32(7), 3148-3155. https://dx.doi.org/10.1109/TNNLS.2020.3009716
Project: MOE2019-T2-1-004
MOE2017-T2-1-001
MOE2015-T2-2-034
Poland-2016/22/E/ST3/00045
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: Reconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary quantum states is challenging as the paradigm of efficient protocols has remained in applying specific techniques for different types of quantum states. Here, we introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network and operates as a comprehensive device for reconstructing an arbitrary quantum state (finite-dimensional or continuous variable). This is achieved with only measuring the average occupation numbers in a single physical setup, without the need of any knowledge of optimum measurement basis or correlation measurements.
URI: https://hdl.handle.net/10356/152419
ISSN: 2162-2388
DOI: 10.1109/TNNLS.2020.3009716
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TNNLS.2020.3009716.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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