A Practical Quantum Reservoir Computing Platform for Quantum Data Processing

Quantum Reservoir Computing (QRC) has the potential to combine the low-latency and energy-efficient machine learning of classical RC with the computing power of complex and high-dimensional quantum dynamics. This is a particularly compelling framework for current quantum hardware, as QRC requires neither complex controls or highly-calibrated operations. Here we present a novel superconducting circuit architecture for QRC, and describe its application to meaningful tasks involving the processing of time-dependent and quantum data, in both simulation and experiment. The device is comprised of an array of transmons coupled to a flux-tunable bus resonator which both mediates all-to-all coupling and provides a robust readout channel. By varying the probe strength and flux bias, the reservoir can be tuned to various processing regimes, enabling a trade-off between nonlinearity and memory capacity through a unified QRC framework. We present a QRC implementation of quantum state tomography, which is performed with near-optimal measurement resources and without the need to calibrate any gate sequences. Our QRC platform and framework poses a practical solution to a wide range of quantum information processing tasks.