Reservoir Computing Approach to Quantum State Measurement

Abstract

Rapid and accurate quantum state measurement is important for maximizing the extracted information from a quantum system. Its optimization plays a critical role in particular for multi-qubit quantum processors deployed for NISQ-era quantum algorithms, quantum simulators, as well as a fault-tolerant variant of quantum computation. Here we propose reservoir computing as a hardware-based solution to quantum state readout of superconducting multi-qubit systems. We consider a small network of Kerr oscillators realized by Josephson parametric oscillators, which can be implemented with minimal device overhead and in the same platform as the measured quantum system. We theoretically analyze its ability to operate as a reservoir computer to classify stochastic time-dependent signals subject to quantum statistical features. We then apply this Kerr network reservoir computer to joint multi-qubit readout. 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. We highlight this by showing more than an order of magnitude reduction in training cost to achieve theoretically optimal fidelity on a two-qubit joint dispersive readout task, when compared with state-of-the-art filtering approaches. Our results indicate that an unoptimized Kerr network reservoir computer can operate as a low latency analog processor at the computational edge and provide rapid and robust processing of quantum state measurement.