Learning quantum ground states in the space of measurement outcomes
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Quantum Physics arXiv:2605.28931 (quant-ph) [Submitted on 27 May 2026] Title:Learning quantum ground states in the space of measurement outcomes Authors:Kartiek Agarwal View a PDF of the paper titled Learning quantum ground states in the space of measurement outcomes, by Kartiek Agarwal View PDF HTML (experimental) Abstract:We investigate variational learning of quantum many-body ground states directly in measurement space using autoregressive neural networks. In particular, we represent quantum states via probability distributions of outcomes over a symmetric informationally complete positive operator-valued measure (SIC-POVM). The probability distribution is encoded in the parameters of an autoregressive neural-network-based on gated recurrent units (GRUs). Ground states are obtained by gradient descent that updates the probability distribution to minimize the energy with respect to a given Hamiltonian, while enforcing positivity constraints that ensure that the distribution of measurement outcomes correspond to a physical quantum state. We analyze the role of constraint enforcement (hierarchy of positivity conditions), variety of neural network architectures (multiple layers, dilation, and modifications of input data) in determining the success of this approach. We benchmark our approach on one-dimensional transverse-field Ising model and the Heisenberg model, along with gapping fields, for system sizes up to L=128, illustrating its efficacy across a wide variety of models. Comments: Subjects: Quantum Physics (quant-ph); Other Condensed Matter (cond-mat.other) Cite as: arXiv:2605.28931 [quant-ph] (or arXiv:2605.28931v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.28931 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kartiek Agarwal [view email] [v1] Wed, 27 May 2026 18:00:01 UTC (1,073 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning quantum ground states in the space of measurement outcomes, by Kartiek AgarwalView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 Change to browse by: cond-mat cond-mat.other 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?)