{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22e02769a88b99abdb1e19d0aa1975fa8/simtech", "tags" : [ "EXC2075","PN8","PN8-3","updated" ], "intraHash" : "2e02769a88b99abdb1e19d0aa1975fa8", "interHash" : "ec1677f9272f456e8e94462a9f2fb4ec", "label" : "Multivariate quantum reservoir computing with discrete and continuous variable systems", "user" : "simtech", "description" : "", "date" : "2026-04-11 15:09:05", "changeDate" : "2026-04-13 14:58:25", "count" : 3, "pub-type": "misc", "year": "2026", "url": "https://arxiv.org/abs/2604.08427", "author": [ "Tobias Fellner","Jonas Merklinger","Christian Holm" ], "authors": [ {"first" : "Tobias", "last" : "Fellner"}, {"first" : "Jonas", "last" : "Merklinger"}, {"first" : "Christian", "last" : "Holm"} ], "eprint" : "2604.08427", "archiveprefix" : "arXiv", "primaryclass" : "quant-ph", "bibtexKey": "fellner2026multivariatequantumreservoircomputing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2092846c63dd93a995bc518498e3282ce/simtech", "tags" : [ "EXC2075","PN8","PN8-3","curated" ], "intraHash" : "092846c63dd93a995bc518498e3282ce", "interHash" : "ba34e41c75ec5ddd320cf4594272c194", "label" : "Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning", "user" : "simtech", "description" : "", "date" : "2026-01-16 07:58:40", "changeDate" : "2026-02-16 15:06:37", "count" : 5, "pub-type": "article", "journal": "Machine Learning: Science and Technology", "year": "2026", "url": "https://doi.org/10.1088/2632-2153/ae365f", "author": [ "Tobias Fellner","David Kreplin","Samuel Tovey","Christian Holm" ], "authors": [ {"first" : "Tobias", "last" : "Fellner"}, {"first" : "David", "last" : "Kreplin"}, {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christian", "last" : "Holm"} ], "abstract": "Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical approaches. However, their practical advantage over established classical methods remains uncertain. In this work, we present a comprehensive benchmark study comparing a range of variational quantum algorithms and classical machine learning models for time series forecasting. We evaluate their predictive performance on three chaotic systems across 27 time series prediction tasks of varying complexity, and ensure a fair comparison through extensive hyperparameter optimization. Our results indicate that, in many cases, quantum models struggle to match the accuracy of simple classical counterparts of comparable complexity. 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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.", "doi" : "10.1088/2632-2153/adc0e2", "bibtexKey": "Tovey_2025" } ] }