Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks

Abstract

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.

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Cited by in corpus (7)

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• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• State Classification via a Random-Walk-Based Quantum Neural Network
• Recurrence in discrete-time quantum stochastic walks
• Quantum hypothesis testing via robust quantum control
• Perturbed graphs achieve unit transport efficiency without environmental noise