Reservoir Computing Approach to Quantum State Measurement

TL;DR

This paper introduces a reservoir computing approach using Josephson parametric oscillators for efficient, high-fidelity quantum state measurement and classification in superconducting multi-qubit systems, enabling low-latency processing with minimal calibration.

Contribution

It proposes a scalable reservoir computing architecture for quantum measurement that outperforms linear filters and simplifies calibration, suitable for integrated cryogenic quantum devices.

Findings

Achieves classification fidelity exceeding optimal linear filters with 2-5 reservoir nodes.

Requires minimal calibration data, as little as one measurement per state.

Demonstrates effective quantum state tomography and parity monitoring.

Abstract

Efficient quantum state measurement is important for maximizing the extracted information from a quantum system. For multi-qubit quantum processors in particular, the development of a scalable architecture for rapid and high-fidelity readout remains a critical unresolved problem. Here we propose reservoir computing as a resource-efficient solution to quantum measurement of superconducting multi-qubit systems. We consider a small network of Josephson parametric oscillators, which can be implemented with minimal device overhead and in the same platform as the measured quantum system. We theoretically analyze the operation of this Kerr network as a reservoir computer to classify stochastic time-dependent signals subject to quantum statistical features. We apply this reservoir computer to the task of multinomial classification of measurement trajectories from joint multi-qubit readout. For a two-qubit dispersive measurement under realistic conditions we demonstrate a classification fidelity reliably exceeding that of an optimal linear filter using only two to five reservoir nodes, while simultaneously requiring far less calibration data \textendash{} as little as a single measurement per state. We understand this remarkable performance through an analysis of the network dynamics and develop an intuitive picture of reservoir processing generally. Finally, we demonstrate how to operate this device to perform two-qubit state tomography and continuous parity monitoring with equal effectiveness and ease of calibration. This reservoir processor avoids computationally intensive training common to other deep learning frameworks and can be implemented as an integrated cryogenic superconducting device for low-latency processing of quantum signals on the computational edge.