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Quantum reservoir computing (QRC) has been proposed as a paradigm forperforming machine learning with quantum processors where the training isefficient in the number of required runs of the quantum processor and takesplace in the classical domain, avoiding the issue of barren plateaus inparameterized-circuit quantum neural networks. It is natural to consider usinga quantum processor based on superconducting circuits to classify microwavesignals that are analog -- continuous in time. However, while theoreticalproposals of analog QRC exist, to date QRC has been implemented usingcircuit-model quantum systems -- imposing a discretization of the incomingsignal in time, with each time point input by executing a gate operation. Inthis paper we show how a quantum superconducting circuit comprising anoscillator coupled to a qubit can be used as an analog quantum reservoir for avariety of classification tasks, achieving high accuracy on all of them. Ourquantum system was operated without artificially discretizing the input data,directly taking in microwave signals. Our work does not attempt to address thequestion of whether QRCs could provide a quantum computational advantage inclassifying pre-recorded classical signals. However, beyond illustrating thatsophisticated tasks can be performed with a modest-size quantum system andinexpensive training, our work opens up the possibility of achieving adifferent kind of advantage than a purely computational advantage:superconducting circuits can act as extremely sensitive detectors of microwavephotons; our work demonstrates processing of ultra-low-power microwave signalsin our superconducting circuit, and by combining sensitive detection with QRCprocessing within the same system, one could achieve a quantumsensing-computational advantage, i.e., an advantage in the overall analysis ofmicrowave signals comprising just a few photons.

Microwave signal processing using an analog quantum reservoir computer