Harnessing quantum back-action for time-series processing

TL;DR

This paper shows that using indirect quantum measurements in quantum reservoir computing enhances performance and efficiency, offering a practical approach to leverage measurement back-action effects.

Contribution

It introduces a method to incorporate and optimize indirect measurements in quantum reservoir computing, improving performance and memory capabilities.

Findings

Optimizing measurement strength improves quantum reservoir computing performance.

Indirect measurements can outperform classical feedback protocols.

Adjusting reservoir Hamiltonian parameters enhances results.

Abstract

Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the system's configuration. In contrast, indirect measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in quantum computing and communication protocols, the potential benefits of indirect measurements in these fields remain largely unexplored. In this work, we demonstrate that incorporating indirect measurements into a quantum machine-learning protocol known as quantum reservoir computing provides advantages in both execution time scaling and overall performance. We analyze different measurement settings by varying the measurement strength across two benchmarking tasks. Our results reveal that carefully optimizing both the reservoir Hamiltonian parameters and the measurement strength can significantly improve the quantum reservoir computing algorithm performance. Furthermore, our approach demonstrates improved memory performance when compared with state-of-the-art classical feedback protocols. This work provides a comprehensive and practical recipe to promote the implementation of indirect measurement-based protocols in quantum reservoir computing. Moreover, our findings motivate further exploration of experimental protocols that leverage the back-action effects of indirect measurements.