Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157029
Title: Creating and concentrating quantum resource states in noisy environments using a quantum neural network
Authors: Krisnanda, Tanjung
Ghosh, Sanjib
Paterek, Tomasz
Liew, Timothy Chi Hin
Keywords: Science::Physics
Issue Date: 2021
Source: Krisnanda, T., Ghosh, S., Paterek, T. & Liew, T. C. H. (2021). Creating and concentrating quantum resource states in noisy environments using a quantum neural network. Neural Networks, 136, 141-151. https://dx.doi.org/10.1016/j.neunet.2021.01.003
Project: MOE2017-T2-1-001
Journal: Neural Networks 
Abstract: Quantum information processing tasks require exotic quantum states as a prerequisite. They are usually prepared with many different methods tailored to the specific resource state. Here we provide a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes. The output of such a system is then superposed with the help of linear mixing where weights and phases are trained in order to obtain desired output quantum states. We explicitly show that our method is robust and can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states. Furthermore, the treatment includes energy decay in the system as well as dephasing and depolarization. Under these noisy conditions we show that the target states are achieved with high fidelity by tuning controllable parameters and providing sufficient strength to the driving of the quantum network. Finally, in very noisy systems, where noise is comparable to the driving strength, we show how to concentrate entanglement by mixing more states in a larger network.
URI: https://hdl.handle.net/10356/157029
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2021.01.003
DOI (Related Dataset): https://doi.org/10.21979/N9/LAKC6C
Rights: © 2021 Elsevier Ltd. All rights reserved. This paper was published in Neural Networks and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20230507
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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