Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical reservoir computing techniques are known to perform best at the “edge of chaos,” or in simpler terms, at a “sweet spot” in which the behavior of systems is neither entirely predictable (i.e., order) nor completely unpredictable (i.e., chaos).
In recent years, some physicists and quantum engineers have been exploring the possibility of realizing a quantum equivalent of classical reservoir computing, known as quantum reservoir computing (QRC). These approaches enable the processing of temporal data and the prediction of events unfolding over time, leveraging high-dimensional quantum states.
Researchers at the University of Tokyo carried out a study investigating how QRC would behave when applied to complex quantum many-body systems, which consist of several interacting quantum particles. Their paper, published in Physical Review Letters, introduces a physics-based framework that could inform the future development of QRC systems.
