Reservoir Computing Approach to Quantum State Measurement

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.

17 pages, 9 figures, and 57 references

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• Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing
• Harnessing Quantum Extreme Learning Machines for image classification
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
• Practical Few-Atom Quantum Reservoir Computing
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Dephasing-assisted diffusive dynamics in superconducting quantum circuits
• Liouvillian skin effect in quantum neural networks
• Robust Quantum Reservoir Computing for Molecular Property Prediction
• Simple Hamiltonian dynamics is a powerful quantum processing resource
• Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics
• Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement
• Estimating many properties of a quantum state via quantum reservoir processing
• Enhanced Predictive Capability for Chaotic Dynamics by Modified Quantum Reservoir Computing
• Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
• Quantum Transport Reservoir Computing
• Neural networks with quantum states of light
• Parameter estimation with one- and two-time measurements on the emission field of the boundary time crystal
• Physical reservoir computing using finitely-sampled quantum systems
• Quantum reservoir computing for predicting and characterizing chaotic maps