%0 Journal Article %1 Tovey_2025 %A Tovey, Samuel %A Fellner, Tobias %A Holm, Christian %A Spannowsky, Michael %D 2025 %I IOP Publishing %J Machine Learning: Science and Technology %K icp peerreviewed postprint %N 1 %P 015068 %R 10.1088/2632-2153/adc0e2 %T Generating quantum reservoir state representations with random matrices %U https://dx.doi.org/10.1088/2632-2153/adc0e2 %V 6 %X We demonstrate a novel approach to reservoir computation measurements using random matrices. We do so to motivate how atomic-scale devices could be used for real-world computational applications. Our approach uses random matrices to construct reservoir measurements, introducing a simple, scalable means of generating state representations. In our studies, two reservoirs, a five-atom Heisenberg spin chain and a five-qubit quantum circuit, perform time series prediction and data interpolation. The performance of the measurement technique and current limitations are discussed in detail, along with an exploration of the diversity of measurements provided by the random matrices. In addition, we explore the role of reservoir parameters such as coupling strength and measurement dimension, providing insight into how these learning machines could be automatically tuned for different problems. This research highlights the use of random matrices to measure simple quantum reservoirs for natural learning devices, and outlines a path forward for improving their performance and experimental realization.

@article{Tovey_2025, abstract = {We demonstrate a novel approach to reservoir computation measurements using random matrices. We do so to motivate how atomic-scale devices could be used for real-world computational applications. Our approach uses random matrices to construct reservoir measurements, introducing a simple, scalable means of generating state representations. In our studies, two reservoirs, a five-atom Heisenberg spin chain and a five-qubit quantum circuit, perform time series prediction and data interpolation. The performance of the measurement technique and current limitations are discussed in detail, along with an exploration of the diversity of measurements provided by the random matrices. In addition, we explore the role of reservoir parameters such as coupling strength and measurement dimension, providing insight into how these learning machines could be automatically tuned for different problems. This research highlights the use of random matrices to measure simple quantum reservoirs for natural learning devices, and outlines a path forward for improving their performance and experimental realization.}, added-at = {2025-04-15T11:58:20.000+0200}, author = {Tovey, Samuel and Fellner, Tobias and Holm, Christian and Spannowsky, Michael}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c82c0ee76304097955291da87e997d76/icp_bib}, description = {Generating quantum reservoir state representations with random matrices - IOPscience}, doi = {10.1088/2632-2153/adc0e2}, interhash = {65f82cf22c6ca53ef1d05dddeaaec8b7}, intrahash = {c82c0ee76304097955291da87e997d76}, journal = {Machine Learning: Science and Technology}, keywords = {icp peerreviewed postprint}, month = mar, number = 1, pages = 015068, publisher = {IOP Publishing}, timestamp = {2025-04-15T11:58:20.000+0200}, title = {Generating quantum reservoir state representations with random matrices}, url = {https://dx.doi.org/10.1088/2632-2153/adc0e2}, volume = 6, year = 2025 }