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.