By Professor Daoyi Dong

Conference Papers

Dong D; Chen C, 2010, 'Quantum-inspired reinforcement learning for decision-making of Markovian state transition', in Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, IEEE, USA, pp. 21 - 26, presented at 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, Hangzhou, China, 15 November 2010 - 16 November 2010, http://dx.doi.org/10.1109/ISKE.2010.5680787

Chen C; Dong D, 2010, 'Complexity Analysis of Quantum Reinforcement Learning', in Proceedings of the 29th Chinese Control Conference, IEEE, USA, presented at Chinese Control Conference, Beijing, China, 29 July 2010 - 31 July 2010, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5572589

Dong D; Petersen IR, 2010, 'Sliding Mode Control of Two-Level Quantum Systems with Bounded Uncertainties', in Proceedings of 2010 American Control Conference, IEEE, Piscataway, NJ, USA, pp. 2446 - 2451, presented at American Control Conference, Baltimore, MD, USA, 30 June 2010 - 02 July 2010, http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05530580

Dong D; Lam J, 2008, 'Rapid incoherent control of a simple quantum system via two-step optimization', in Proceedings of the 27th Chinese Control Conference Ccc, pp. 511 - 515, http://dx.doi.org/10.1109/CHICC.2008.4605386

Chen C; Yang P; Zhou X; Dong D, 2008, 'A Quantum-inspired Q-learning algorithm for indoor robot navigation', in Proceedings of 2008 IEEE International Conference on Networking Sensing and Control Icnsc, pp. 1599 - 1603, http://dx.doi.org/10.1109/ICNSC.2008.4525476

Dong D; Chen C; Li H, 2007, 'Reinforcement strategy using quantum amplitude amplification for robot learning', in Proceedings of the 26th Chinese Control Conference Ccc 2007, pp. 571 - 575, http://dx.doi.org/10.1109/CHICC.2006.4347206

Chen Z; Dong D; Zhang C, 2006, 'Quantum Control Based on Quantum Information', in 2006 Chinese Control Conference, IEEE, pp. 2121 - 2126, presented at 2006 Chinese Control Conference, 07 August 2006 - 11 August 2006, http://dx.doi.org/10.1109/chicc.2006.280930

Chen Z; Dong D; Zhang C, 2006, 'Quantum control based on quantum information', in 2006 CHINESE CONTROL CONFERENCE, VOLS 1-5, IEEE, PEOPLES R CHINA, Harbin, pp. 1258 - +, presented at 25th Chinese Control Conference, PEOPLES R CHINA, Harbin, 07 August 2006 - 10 August 2006, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000243873602068&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

Dong D; Chen C; Chen Z; Zhang C, 2006, 'Estimation-based information acquisition in quantum feedback control', in DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, WATAM PRESS, PEOPLES R CHINA, Huhhot, pp. 1204 - 1208, presented at International Conference on Complex Systems and Applications, PEOPLES R CHINA, Huhhot, 16 June 2006 - 18 June 2006, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000245061700063&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

Chen C; Dong D; Chen Z, 2006, 'Grey reinforcement learning for incomplete information processing', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 399 - 407, http://dx.doi.org/10.1007/11750321_38

Dong D; Chen C; Zhang C; Chen Z, 2005, 'An autonomous mobile robot based on quantum algorithm', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 393 - 398, http://dx.doi.org/10.1007/11596448_57

Dong D; Zhang C; Chen Z, 2005, 'Quantum feedback control using quantum cloning and state recognition1', in IFAC Proceedings Volumes IFAC Papersonline, pp. 195 - 200

Dong DY; Chen ZH, 2005, 'Perturbation theory for quantum control with weak input', in 2005 INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), VOLS 1 AND 2, IEEE, HUNGARY, Hungarian Acad Sci, Budapest, pp. 736 - 740, presented at 5th International Conference on Control and Automation, HUNGARY, Hungarian Acad Sci, Budapest, 26 June 2005 - 29 June 2005, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000232156500131&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

Dong D; Chen C; Chen Z, 2005, 'Quantum reinforcement learning', in Lecture Notes in Computer Science, pp. 686 - 689, http://dx.doi.org/10.1007/11539117_97

Dong D; Chen Z, 2004, 'Research on modeling and simulation of quantum control systems', in Proceedings of the World Congress on Intelligent Control and Automation WCICA, pp. 276 - 279

Wolkowicz MD; Smith JA; Dong D; Dagli SS, 1997, 'Reactor chemistry & microstructure of polypropylene grafts', in ANTEC'97 - PLASTICS SAVING PLANET EARTH, CONFERENCE PROCEEDINGS, VOLS 1 - 3, SOC PLASTICS ENGINEERS, CANADA, TORONTO, pp. 1664 - 1670, presented at 55th Annual Technical Conference of the Society-of-Plastics-Engineers - Plastics Saving Planet Earth (ANTEC 97), CANADA, TORONTO, 27 April 1997 - 02 May 1997

DeMaio VV; Dong D, 1997, 'The effect of chain structure on melt strength of polypropylene and polyethylene', in ANTEC'97 - PLASTICS SAVING PLANET EARTH, CONFERENCE PROCEEDINGS, VOLS 1 - 3, SOC PLASTICS ENGINEERS, CANADA, TORONTO, pp. 1512 - 1516, presented at 55th Annual Technical Conference of the Society-of-Plastics-Engineers - Plastics Saving Planet Earth (ANTEC 97), CANADA, TORONTO, 27 April 1997 - 02 May 1997

Gelbard G; Sherrington DC; Breton F; Benelmoudeni M; Charreyre MT; Dong D, 1994, 'Polymers with ligated peroxotungstic units: Organophosphoryl macroligands for the catalytic epoxidation of alkenes.', in Pittman CU; Carraher CE; Zeldin M; Sheats JE; Culbertson BM (eds.), METAL-CONTAINING POLYMERIC MATERIALS, PLENUM PRESS DIV PLENUM PUBLISHING CORP, DC, WASHINGTON, pp. 265 - 275, presented at International Symposium on Metal-Containing Polymeric Materials, at the 208th National American-Chemical-Society Meeting, DC, WASHINGTON, 20 August 1994 - 25 August 1994

GELBARD G; BRETON F; CHARREYRE MT; DONG D, 1991, 'POLYPYRIDINE-BASED CATALYSTS - EPOXIDATION OF OLEFINS WITH SUPPORTED PEROXOTUNGSTIC COMPLEXES', in MAKROMOLEKULARE CHEMIE-MACROMOLECULAR SYMPOSIA, HUTHIG & WEPF VERLAG, ITALY, SIENA, pp. 353 - 361, presented at 4TH INTERNATIONAL SYMP ON MACROMOLECULAR METAL COMPLEXES, ITALY, SIENA, 30 September 1991 - 05 October 1991, http://dx.doi.org/10.1002/masy.19920590129

Conference Abstracts

Zhu Y; Tian F; Dong D; Young J; Lai J, 2018, 'Study of fish self- adapting behaviour in Karman vortex street using reinforcement learning', in The 13th World Congress in Computational Mechanics, presented at The 13th World Congress in Computational Mechanics, 22 July 2018 - 27 July 2018

Reports

Dong D, 2020, Quantum Cybernetics Technical Committee Reports: Investigating the Role of Quantum Effects in Regulating Quantum and Classical Systems, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, http://dx.doi.org/10.1109/MSMC.2019.2952458, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000528940200005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

Dong D; Shi G; Vuglar S; James MR, 2017, Special issue on quantum control Dedicated to the occasion of Prof. Ian Petersen’s 60th birthday, Springer Nature, http://dx.doi.org/10.1007/s11768-017-6200-4

Preprints

Ma H; Qi B; Petersen IR; Wu R-B; Rabitz H; Dong D, 2025, Machine Learning for Estimation and Control of Quantum Systems, http://dx.doi.org/10.48550/arxiv.2503.03164

Xiao S; Liang W; Wang Y; Dong D; Petersen IR; Ugrinovskii V, 2025, Simultaneous estimations of quantum state and detector through multiple quantum processes, http://dx.doi.org/10.48550/arxiv.2502.11772

Hong Q-Q; Dong D; Henriksen NE; Nori F; He J; Shu C-C, 2025, Precise Quantum Control of Molecular Rotation Toward a Desired Orientation, http://dx.doi.org/10.48550/arxiv.2502.10196

Selim A; Mo H; Pota H; Dong D, 2024, Adaptive BESS and Grid Setpoints Optimization: A Model-Free Framework for Efficient Battery Management under Dynamic Tariff Pricing, http://dx.doi.org/10.48550/arxiv.2408.09989

Liu Y-H; Zeng Y; Tan Q-S; Dong D; Nori F; Liao J-Q, 2024, Optimal control of linear Gaussian quantum systems via quantum learning control, http://dx.doi.org/10.48550/arxiv.2406.05597

Wang Y; Wang X; Qi B; Dong D, 2024, Supervised Learning Guarantee for Quantum AdaBoost, http://dx.doi.org/10.48550/arxiv.2402.02376

Wang X; Qi B; Wang Y; Dong D, 2023, EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers, http://dx.doi.org/10.48550/arxiv.2311.01120

Ma H; Mooney GJ; Petersen IR; Hollenberg LCL; Dong D, 2023, Quantum autoencoders using mixed reference states, http://dx.doi.org/10.48550/arxiv.2309.15582

Liu Y; Dong D; Petersen IR; Yonezaw H, 2023, Fault-tolerant $H^\infty$ control for optical parametric oscillators with pumping fluctuations, http://dx.doi.org/10.48550/arxiv.2307.14583

Liu Y; Dong D; Kuang S; Petersen IR; Yonezawa H, 2023, Two-step feedback preparation of entanglement for qubit systems with time delay, http://dx.doi.org/10.48550/arxiv.2307.14599

Ma H; Sun Z; Dong D; Chen C; Rabitz H, 2023, Tomography of Quantum States from Structured Measurements via quantum-aware transformer, http://dx.doi.org/10.48550/arxiv.2305.05433

Bao L; Qi B; Nori F; Dong D, 2023, Exponential sensitivity revival of noisy non-Hermitian quantum sensing with two-photon drives, http://dx.doi.org/10.48550/arxiv.2303.16575

Wang Y; Qi B; Ferrie C; Dong D, 2023, Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization, http://dx.doi.org/10.48550/arxiv.2302.06858

Shindi O; Yu Q; Girdhar P; Dong D, 2023, Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning, http://dx.doi.org/10.48550/arxiv.2302.02371

Fan L-B; Shu C-C; Dong D; He J; Henriksen NE; Nori F, 2022, Quantum Coherent Control of a Single Molecular-Polariton Rotation, http://dx.doi.org/10.48550/arxiv.2212.11649

Jiang C; Pan Y; Wu Z-G; Gao Q; Dong D, 2022, Robust optimization for quantum reinforcement learning control using partial observations, http://dx.doi.org/10.48550/arxiv.2206.14420

Wang Z; Chen C; Dong D, 2022, A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning, http://dx.doi.org/10.48550/arxiv.2205.10787

Liu J; Wang Z; Chen C; Dong D, 2022, Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning, http://dx.doi.org/10.48550/arxiv.2204.07729

Xie D; Wang Z; Chen C; Dong D, 2022, Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation, http://dx.doi.org/10.48550/arxiv.2203.02896

Dong D; Petersen IR, 2022, Quantum estimation, control and learning: opportunities and challenges, http://dx.doi.org/10.48550/arxiv.2201.05835

Ma H; Dong D; Petersen IR; Huang C-J; Xiang G-Y, 2021, Neural networks for quantum state tomography with constrained measurements, http://dx.doi.org/10.48550/arxiv.2111.09504

Bao L; Qi B; Dong D, 2021, Exponentially-enhanced Quantum Non-Hermitian Sensing via Optimized Coherent Drive, http://dx.doi.org/10.48550/arxiv.2109.04040

Li Y; Aghvami AH; Dong D, 2021, Path Planning for Cellular-Connected UAV: A DRL Solution with Quantum-Inspired Experience Replay, http://dx.doi.org/10.48550/arxiv.2108.13184

Chen Y; Pan Y; Dong D, 2021, Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations, http://dx.doi.org/10.48550/arxiv.2108.08659

Bao L; Qi B; Dong D; Nori F, 2021, Fundamental limits for reciprocal and non-reciprocal non-Hermitian quantum sensing, http://dx.doi.org/10.48550/arxiv.2104.10822

Dong D, 2021, Learning Control of Quantum Systems, http://dx.doi.org/10.48550/arxiv.2101.07461

Ma H; Dong D; Ding SX; Chen C, 2020, Curriculum-based Deep Reinforcement Learning for Quantum Control, http://dx.doi.org/10.48550/arxiv.2012.15427

Wang Z; Chen C; Dong D, 2020, Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments, http://dx.doi.org/10.48550/arxiv.2010.04605

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