Quantum Annealing Systems As Reservoirs I: Design and Performance

APS  ·  2022

Gerasimos Angelatos; Hakan Tureci; Fangjun Hu; Saeed Khan;

HIGHLIGHT

Here we propose a general algorithm to operate an existing Quantum Annealing System as a Reservoir (QASAR), and discuss its application to static pattern recognition and dynamic signal processing tasks, including signal classification and channel equalization.

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ABSTRACT

Modern quantum processors offer an unprecedented number of quantum degrees of freedom which can be controlled and read-out. Although NISQ constraints limit the utility of such devices for conventional quantum machine learning applications, they can readily serve as the physical platform of a reservoir computer or extreme learning machine. Here we propose a general algorithm to operate an existing Quantum Annealing System as a Reservoir (QASAR), and discuss its application to static pattern recognition and dynamic signal processing tasks, including signal classification and channel equalization. We find robust performance across a variety of tasks, with state-of-the-art resource efficiency in terms of qubits, circuit runs, and training overhead. Unlike conventional quantum machine learning approaches, the dissipation in these fundamentally open systems is an essential ingredient to successful signal processing. We demonstrate how to readily increase this device's feature size and consequent performance through spatial multiplexing and the measurement of higher moments, at the cost of only a modest increase in circuit runs.

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