Quantum state discrimination on reconfigurable noise-robust quantum networks

Abstract

A fundamental problem in quantum information processing is the discrimination among 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.

Machine learning classification of non-Markovian noise disturbing quantum dynamics

“…To effectively present our novel approach and demonstrate its efficacy in discriminating Markovian and non-Markovian noise sources, we focus on the single-particle case and specifically on the dynamics of a single quantum walker randomly moving on a graph G [29][30][31][32][33], as generated by a stochastic Schrödinger equation. In this context, by training a properly-designed artificial neural network model via the probabilities that the quantum walker is in each node of the graph G at discrete time instants, we are going to show that it is possible to accurately discriminate between different noise sources and identify the possible presence of noise time-correlation, by observing only the single realisations of the stochastic quantum dynamics.…”

Experimental multi-state quantum discrimination through a Quantum network

“…Other proposals involve the exploitation of an auxiliary system [18], in order to increase the dimension of the system to be discriminated, or consist of encoding the states in a complex modal structure [19]. Since adaptive strategies have been proved to be effective, although quite expensive in terms of resources, recent theoretical efforts have been developed to apply neural networks models [20,21] and Machine Learning (ML) protocols [22] to QSD problems. In addition to the attempts mentioned above, a dynamical approach based on information processing via Quantum Walks (QWs) has been fancied in the last years [23,24].…”

“…In addition to the attempts mentioned above, a dynamical approach based on information processing via Quantum Walks (QWs) has been fancied in the last years [23,24]. The network depicted in [21], relying on a generalization of QWs, Quantum Stochastic Walks (QSWs) [25], frames a very intuitive model of information processing as well as a wide applicability. In the present work, we experimentally implement a discrimination protocol tightly related to the one proposed in [21].…”

“…The network depicted in [21], relying on a generalization of QWs, Quantum Stochastic Walks (QSWs) [25], frames a very intuitive model of information processing as well as a wide applicability. In the present work, we experimentally implement a discrimination protocol tightly related to the one proposed in [21]. We demonstrate the effectiveness of a receiver relying on a QSW-like evolution and we experimentally analyze the dynamics of information in a time-binned extraction protocol.…”

“…Theoretical and experimental framework -The fundamental aim of our work is to experimentally reproduce a network featured by a 2r-2r-2 topology (according to the nomenclature adopted in [21]) and depicted in Fig. 1: two input nodes, namely {1, 2}, are linked in an undirected way with two nodes in the intermediate layer, namely {3, 4}, which are in turn linked in a directed way with two output nodes, namely {5, 6}, acting as sink nodes.…”