Microwave signal processing using an analog quantum reservoir computer

Alen Senanian (), Sridhar Prabhu, Vladimir Kremenetski, Saswata Roy, Yingkang Cao, Jeremy Kline, Tatsuhiro Onodera, Logan G. Wright, Xiaodi Wu, Valla Fatemi and Peter L. McMahon ()
Additional contact information
Alen Senanian: Cornell University
Sridhar Prabhu: Cornell University
Vladimir Kremenetski: Cornell University
Saswata Roy: Cornell University
Yingkang Cao: University of Maryland
Jeremy Kline: Cornell University
Tatsuhiro Onodera: Cornell University
Logan G. Wright: Cornell University
Xiaodi Wu: University of Maryland
Valla Fatemi: Cornell University
Peter L. McMahon: Cornell University

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on microwave superconducting circuits to classify microwave signals that are analog—continuous in time. However, while there have been theoretical proposals of analog QRC, to date QRC has been implemented using the circuit model—imposing a discretization of the incoming signal in time. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our work demonstrates processing of ultra-low-power microwave signals within our superconducting circuit, a step towards achieving a quantum sensing-computational advantage on impinging microwave signals.

Date: 2024
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DOI: 10.1038/s41467-024-51161-8

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