Abstract
Realizing the promise of quantum information processing remains a daunting task, given the omnipresence of noise and error. Adapting noise-resilient classical computing modalities to quantum mechanics may be a viable path towards near-term applications in the noisy intermediate-scale quantum era. Here, we propose continuous variable quantum reservoir computing in a single nonlinear oscillator. Through numerical simulation of our model we demonstrate quantum-classical performance improvement, and identify its likely source: the nonlinearity of quantum measurement. Beyond quantum reservoir computing, this result may impact the interpretation of results across quantum machine learning. We study how the performance of our quantum reservoir depends on Hilbert space dimension, how it is impacted by injected noise, and briefly comment on its experimental implementation. Our results show that quantum reservoir computing in a single nonlinear oscillator is an attractive modality for quantum computing on near-term hardware.
Highlights
Over the last several decades, quantum information science has emerged as a transformative framework for information processing, from high-performance computing to communication and cryptography [1]
We present quantum reservoir computing (QRC) in a continuous-variable system, with a reservoir formed by a single nonlinear oscillator, and contrast to classical reservoir computing (CRC) with the equivalent classical reservoir
We demonstrate via numerical simulation an improvement in performance of QRC compared to CRC for the same classical task of sine phase estimation
Summary
Over the last several decades, quantum information science has emerged as a transformative framework for information processing, from high-performance computing to communication and cryptography [1]. By using a continuous-variable system, we reduce the costly repetitions necessary to obtain accurate measurement of expectation values, an issue that affects the run time of discrete-variable quantum machine learning approaches [17] We expect this to be an advantage that continuous-variable reservoirs will show over discretevariable ones in practical implementation. We demonstrate via numerical simulation an improvement in performance of QRC compared to CRC for the same classical task of sine phase estimation. This improvement is both in average error for small training set sizes and a reduction in performance spread across reservoir parameters.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
