Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks

A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Stochastic Walk. In particular, the structure of t...

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Bibliographic Details
Published inarXiv.org
Main Authors Nicola Dalla Pozza, Caruso, Filippo
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.03.2020
Subjects
Computer simulation
Data processing
Decision analysis
Decision theory
Discrimination
Machine learning
Neural networks
Nodes
Optimization
Physics - Quantum Physics
Quantum phenomena
Quantum theory
Robustness (mathematics)
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Summary:A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Stochastic Walk. In particular, the structure of the graph mimics those of neural networks, with the quantum states to discriminate encoded on input nodes and with the discrimination obtained on the output nodes. We optimize the parameters of the network to obtain the highest probability of correct discrimination. Numerical simulations show that after a transient time the probability of correct decision approaches the theoretical optimal quantum limit. These results are confirmed analytically for small graphs. Finally, we analyze the robustness and reconfigurability of the network for different set of quantum states, and show that this architecture can pave the way to experimental realizations of our protocol as well as novel quantum generalizations of deep learning.
ISSN:2331-8422
DOI:10.48550/arxiv.2003.11586