Experimental quantum reservoir computing with a circuit quantum electrodynamics system

TL;DR

This paper demonstrates a hardware-efficient quantum reservoir computing system using circuit quantum electrodynamics, capable of classifying classical tasks with fewer resources than classical neural networks, supported by experimental and simulation results.

Contribution

It presents the first experimental implementation of quantum reservoir computing with a simple circuit QED system, achieving nonlinear feature extraction and classification.

Findings

Successful classification of classical tasks with minimal hardware resources

Large number of nonlinear features obtained from a single transmon system

Kerr nonlinearity enhances reservoir performance

Abstract

Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimization. Learning is performed in a single step on the output features measured from the quantum system. Various implementations of quantum reservoir computing have been explored in simulations, with different measured features. Although simulations have shown that quantum reservoirs present advantages in performance compared to classical reservoirs, experimental implementations have remained scarce. This is due to the challenge of obtaining a large number of output features that are nonlinear transformations of the input data. In this work, we show that even with a circuit quantum electrodynamics system as simple as a single transmon coupled to a readout resonator, we can implement a proof-of-concept realization of quantum reservoir computing. We obtain a large number of nonlinear features from a single physical system by encoding the input data in the amplitude of a coherent drive and measuring the cavity state in the Fock basis. We demonstrate classification of two classical tasks with significantly smaller hardware resources and fewer measured features compared to classical neural networks. Our experimental results are supported by numerical simulations that show additional Kerr nonlinearity is beneficial to reservoir performance. Our work demonstrates a hardware-efficient quantum neural network implementation that can be further scaled up and generalized to other quantum machine learning models.