Parametrized-circuit-free quantum regression with variance regularization
This work reduces the resource overhead of quantum regression by eliminating variational circuit training, offering a scalable path for near-term quantum devices while maintaining accuracy in symmetry-rich problems.
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Quantum Physics arXiv:2607.02696 (quant-ph) [Submitted on 2 Jul 2026] Title:Parametrized-circuit-free quantum regression with variance regularization Authors:Yerassyl Balkybek, Andrey Kardashin, Vladimir V. Palyulin, Konstantin Antipin View a PDF of the paper titled Parametrized-circuit-free quantum regression with variance regularization, by Yerassyl Balkybek and 3 other authors View PDF HTML (experimental) Abstract:Quantum regression tasks for predicting properties of quantum states are commonly addressed using variational quantum algorithms. While variational quantum circuits are highly expressive and allow to achieve reasonable accuracy, training these circuits may demand a considerable amount of time and resources. In this work, we propose an approach of constructing problem-specific quantum regression models with encoding relevant symmetries and regularizing the variance. The proposed method is based on finding the coefficients of the linear combination of suitably chosen observables. Although it requires the knowledge of the symmetries of the problem in question, the method does not involve parameterized quantum circuits, and the training is done efficiently once the observables are measured. We demonstrate this method on two examples: Prediction of the transverse field strength in the Ising model, and quantification of entanglement in bipartite qubit systems. Our approach is accurate and less resource-intensive than conventional variational methods. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.02696 [quant-ph] (or arXiv:2607.02696v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.02696 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yerassyl Balkybek [view email] [v1] Thu, 2 Jul 2026 18:38:23 UTC (4,367 KB) Full-text links: Access Paper: View a PDF of the paper titled Parametrized-circuit-free quantum regression with variance regularization, by Yerassyl Balkybek and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-07 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)