Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot

Nature Physics (2026)Cite this article The work required to drive a system from one state to another comprises both the equilibrium free energy difference and the dissipation associated with irreversibility. As physical processes—such as computing—approach fast limits, calculating this excess dissipation becomes increasingly critical. Yet, precisely quantifying dissipation, more specifically, entropy production, in strongly driven, time-dependent, realistic nanoscale systems remains a considerable challenge. Consequently, previous studies have largely been limited to either idealized Markovian systems under time-dependent driving or non-Markovian steady-state systems under constant driving. Here we measure the full dynamics of trajectory-level entropy production in a non-stationary, non-Markovian material arising from time-dependent driving. We use machine learning to extract the entropy produced by a quantum dot stochastically blinking under a stepwise control protocol. The entropy produced corresponds to the loss of memory in the material as the carrier distribution evolves. In addition, our approach quantifies both information insertion and dissipation under a quenched protocol. 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F.L. acknowledges support from the US Department of Energy, Office of Science, Basic Energy Sciences, CPIMS Program (Award No. DE-SC0026181). We thank P. H. Bucksbaum for generously providing access to his laboratory and the laser facilities used in this work. We are grateful to H. Qin and X. Peng for supplying the QD samples and to Z. Jiang (Karunadasa Group) for providing the PMMA for sample preparation. We also acknowledge valuable discussions with Y. Huang, Y. Jiang and Y. Du regarding the QD identification algorithm and machine learning approaches.These authors contributed equally: Yuejun Shen, Chutian Chen.Department of Materials Science and Engineering, Stanford University, Stanford, CA, USAYuejun Shen, Jiaojian Shi & Aaron M. LindenbergStanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USAYuejun Shen, Jiaojian Shi & Aaron M. LindenbergInstitute of Physics, University of Amsterdam, Amsterdam, The NetherlandsChutian ChenStanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, CA, USAHaoran Ma, Christian Heide & Aaron M. LindenbergDepartment of Physics, Stanford University, Stanford, CA, USAHaoran MaDepartment of Chemistry, Stanford University, Stanford, CA, USAAshley P. Saunders, Fang Liu & Grant M. RotskoffDepartment of Physics, University of Central Florida, Orlando, FL, USAChristian HeideCREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL, USAChristian HeideDepartment of Chemistry, University of Washington, Seattle, WA, USAJiaojian ShiSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarSearch author on:PubMed Google ScholarY.S., J.S. and A.M.L. conceived the project and initiated the experimental effort. C.C., Y.S. and G.M.R. developed the theoretical framework and constructed the model. Y.S. and H.M. performed the data acquisition. Y.S., A.P.S. and F.L. were responsible for sample preparation. C.H. optimized the laser system. Y.S. and C.C. led the writing of the paper. A.M.L. supervised the project.Correspondence to Aaron M. Lindenberg.The authors declare no competing interests.Nature Physics thanks Paul Eastham, Richard Geilhufe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Experimental data are split into train and test sets. Model parameters are optimized by minimizing the loss (top left) on the train data and validated by comparing losses on train and test sets. Bottom left: field-off parameters are optimized first and used for subsequent field-on optimization.Supplementary Notes 1–7, Figs. 1–12 and Table 1.A viscous system under pulsed excitations.PL measurement of QD blinking under a periodically applied NIR field.Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsShen, Y., Chen, C., Ma, H. et al. Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot. Nat. Phys. (2026). https://doi.org/10.1038/s41567-026-03177-8Download citationReceived: 27 June 2025Accepted: 08 January 2026Published: 09 February 2026Version of record: 09 February 2026DOI: https://doi.org/10.1038/s41567-026-03177-8Anyone you share the following link with will be able to read this content:Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative