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Reservoir Computing Approach to Quantum State Measurement
Türeci, Hakan - Princeton University
Presentation on Thursday, Oct. 15, 2020, noon
Location: Online
Reservoir computing is an artificial neural network approach developed over the past two decades for processing time-dependent signals, used successfully for applications such as classification, forecasting and feedback control. In our work we extend this framework to the processing of signals resulting from the measurement of a quantum mechanical system. The motivation is to substantially reduce the latency in the measurement process and identify new modalities for extracting information about multi-qubit systems. I will discuss some preliminary results from our work-in-progress on the implementation and theoretically expected performance of a cryogenic superconducting reservoir processor for the joint dispersive readout of a multi-qubit system. Most notably we find that the training of a reservoir processor is substantially faster than the calibration of a readout system through an optimal linear filter, the established approach implemented in superconducting quantum computers today.