A Mutual Information-based Metric for Temporal Expressivity and Trainability Estimation in Quantum Policy Gradient Pipelines

Quantum Physics arXiv:2512.05157 (quant-ph) [Submitted on 4 Dec 2025] Title:A Mutual Information-based Metric for Temporal Expressivity and Trainability Estimation in Quantum Policy Gradient Pipelines Authors:Jaehun Jeong, Donghwa Ji, Junghee Ryu, Kabgyun Jeong View a PDF of the paper titled A Mutual Information-based Metric for Temporal Expressivity and Trainability Estimation in Quantum Policy Gradient Pipelines, by Jaehun Jeong and 3 other authors View PDF HTML (experimental) Abstract:In recent years, various limitations of conventional supervised learning have been highlighted, leading to the emergence of reinforcement learning -- and, further, quantum reinforcement learning that exploits quantum resources such as entanglement and superposition -- as promising alternatives. Among the various reinforcement learning methodologies, gradient-based approaches, particularly policy gradient methods, are considered to have many benefits. Moreover, in the quantum regime, they also have a profit in that they can be readily implemented through parameterized quantum circuits (PQCs). From the perspective of learning, two indicators can be regarded as most crucial: expressivity and, for gradient-based methods, trainability. While a number of attempts have been made to quantify the expressivity and trainability of PQCs, clear efforts in the context of reinforcement learning have so far been lacking. Therefore, in this study, we newly define the notion of expressivity suited to reinforcement learning and demonstrate that the mutual information between action distribution and reward-signal distribution can, in certain respects, indicate information about both expressivity and trainability. Such research is valuable in that it provides an easy criterion for choosing among various PQCs employed in reinforcement learning, and further, enables the indirect estimation of learning progress even in black-box settings where the agent's achievement aligned with the episodes cannot be explicitly evaluated. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2512.05157 [quant-ph] (or arXiv:2512.05157v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.05157 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jaehun Jeong [view email] [v1] Thu, 4 Dec 2025 07:07:24 UTC (4,689 KB) Full-text links: Access Paper: View a PDF of the paper titled A Mutual Information-based Metric for Temporal Expressivity and Trainability Estimation in Quantum Policy Gradient Pipelines, by Jaehun Jeong and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 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?)