Kerr Network Reservoir Computing for Quantum State Measurement

Quantum measurement protocols combining fast high fidelity readout with rapid calibration are increasingly important as device sizes grow in the NISQ era. Here we propose reservoir processing as a hardware-based solution to superconducting qubit readout. We consider a small network of Kerr oscillators, implementable with minimal hardware overhead, and theoretically analyze its ability to operate as a reservoir computer and classify stochastic time-dependent signals subject to quantum statistical features. We then apply this Kerr network reservoir computer to multi-qubit readout in a regime of multiplexing that presents maximal cross-talk between the readout channels. We demonstrate rapid multinomial classification of these measurement trajectories with a fidelity exceeding that of conventional filtering approaches. This reservoir computing framework avoids computationally expensive training standard for neural-networks, and requires orders-of-magnitude less training data than an optimal matched filter for the same task. Our results indicate that an unoptimized Kerr network can operate as a low latency analog processor at the computational edge and provide rapid and robust processing of quantum state measurement.