Quantum Reservoir Computing with Neutral Atom Arrays

With the development of quantum computers, quantum machine learning has recently attracted much attention. While it has been considered a promising application for near-term quantum computers, current quantum machine learning methods require large quantum resources and suffer from gradient vanishing issues. To alleviate this, we propose a general-purpose quantum reservoir computing algorithm for neutral atom quantum simulators that is resource-frugal, noise-resilient, and scalable. We implement our proposal on QuEra's field-programmable qubit array, Aquila, and observe state-of-the-art performance on several practical machine-learning tasks.