Abstract
We demonstrate a novel approach to reservoir computer measurements through the use of a simple
quantum system and random matrices to motivate how atomic-scale devices might be used for
real-world computing applications. In our approach, random matrices are used to construct reservoir
measurements, introducing a simple, scalable means for producing state descriptions. In our studies,
systems as simple as a five-atom Heisenberg spin chain are used to perform several tasks, including
time series prediction and data interpolation. The performance of the measurement technique as
well as their current limitations are discussed in detail alongside an exploration of the diversity of
measurements yielded by the random matrices. Additionally, we explore the role of the parameters of
the spin chain, adjusting coupling strength and the measurement dimension, yielding insights into how
these learning machines might be automatically tuned for different problems. This research highlights
the use of random matrices as measurements of simple quantum systems for natural learning devices
and outlines a path forward for improving their performance and experimental realisation.
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