Time-series quantum reservoir computing with weak and projective measurements

Abstract

Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies.

Financial Policy for Promoting Equality Education in Rural Areas: Two Key Policy Implementation in China

“…For future research, reservoir computing is suitable for temporal pattern recognition, classification, and generation [34,69,70]. While we have proposed a criteria for analyzing the statistical fluctuation of quantum reservoir outputs in parameter estimation, it is important to do so in temporal information processing tasks such as temporal quantum tomography [71] and nonlinear temporal machine learning [72].…”

Enhancing Signal Recognition Accuracy in Delay-Based Optical Reservoir Computing: A Comparative Analysis of Training Algorithms

“…D. Verstraeten later synthesized these concepts in 2007, unifying them under the term "reservoir computing" [4]. RC has significantly streamlined the training process of recurrent neural networks and demonstrated impressive performance in various domains, including time series prediction [5], speech recognition [6], and pattern recognition [7]. RC has also expanded the potential for hardware implementation, thanks to its streamlined structure.…”