Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems
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Quantum Physics arXiv:2606.03147 (quant-ph) [Submitted on 2 Jun 2026] Title:Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems Authors:G. E. L. Pexe, L. A. M. Rattighieri, P. M. Prado, A. R. Fritsch, F. F. Fanchini View a PDF of the paper titled Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems, by G. E. L. Pexe and 4 other authors View PDF HTML (experimental) Abstract:This perspective article analyzes the potential and critical challenges of employing quantum optimization algorithms to investigate phase transitions in quantum many-body systems during the Noisy Intermediate-Scale Quantum era. The simulation of strongly correlated systems is frequently intractable on classical computers due to the exponential growth of the Hilbert space and the fermionic sign problem. In this context, we review and compare the performance of traditional Variational Quantum Algorithms, such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm, against emerging heuristic approaches, specifically Feedback-based Quantum Algorithms, such as FALQON. We explore the applicability of these methods in the study of open phenomena in condensed matter physics, including Deconfined Quantum Criticality, strange metals, Many-Body Localization, topological phase transitions, and quantum spin liquids. We discuss how fundamental operational bottlenecks, notably expressibility- and noise-induced barren plateaus, severely compromise gradient-based optimization. We conclude that deterministic feedback-guided methods provide geometrically more robust trajectories for navigating the energy landscape of these systems, arguing that further advancement in the field will rely on deep hybridization and physics-informed circuit co-design towards fault tolerance. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.03147 [quant-ph] (or arXiv:2606.03147v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.03147 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Guilherme Pexe [view email] [v1] Tue, 2 Jun 2026 04:42:30 UTC (327 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems, by G. E. L. Pexe and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-06 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?)