The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains
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Quantum Physics arXiv:2601.06303 (quant-ph) [Submitted on 9 Jan 2026] Title:The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains Authors:Sofía Perón Santana, Ariel Fiuri, Martín Domínguez, Omar Osenda View a PDF of the paper titled The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains, by Sof\'ia Per\'on Santana and 3 other authors View PDF HTML (experimental) Abstract:In recent years, control methods based on different optimization techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times are mandatory to avoid the deleterious effects of multiple sources of decoherence that spoil the transmission process. In particular, using Reinforcement Learning to devise sequences of step-wise external controls provides good transfer policies at short transmission times. We present two approaches to control the transmission of quantum states in qubit chains using external controls to force the dynamical evolution of the chain state. The first approach relies on the well-known Genetic Algorithm to generate a sequence of external controls, while the second approach uses a variant of Reinforcement Learning. The Genetic algorithm achieves excellent transmission fidelity at as short transmission times as Reinforcement Learning, surpassing the fidelities achieved by the latter method. Nevertheless, the Reinforcement Learning method offers robust control policies when the control pulses are noisy enough, owing to an imperfect timing of the pulses, deficient control devices, or other sources of phase decoherence. We present the regime where each method is best suited to control the transmission of arbitrary qubit states. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.06303 [quant-ph] (or arXiv:2601.06303v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.06303 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Omar Osenda [view email] [v1] Fri, 9 Jan 2026 20:43:33 UTC (9,631 KB) Full-text links: Access Paper: View a PDF of the paper titled The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains, by Sof\'ia Per\'on Santana and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)