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, Peter L McMahon
Author Information
Alen Senanian: Department of Physics, Cornell University, Ithaca, NY, USA. As3656@cornell.edu. ORCID
Sridhar Prabhu: Department of Physics, Cornell University, Ithaca, NY, USA. ORCID
Vladimir Kremenetski: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA.
Saswata Roy: Department of Physics, Cornell University, Ithaca, NY, USA. ORCID
Yingkang Cao: Department of Computer Science, University of Maryland, College Park, MD, USA. ORCID
Jeremy Kline: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA.
Tatsuhiro Onodera: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA. ORCID
Logan G Wright: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA.
Xiaodi Wu: Department of Computer Science, University of Maryland, College Park, MD, USA.
Valla Fatemi: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA. ORCID
Peter L McMahon: School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA. pmcmahon@cornell.edu. ORCID
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.
References
Nature. 2017 Mar 3;543(7644):171-174
[PMID: 28277529]
