PUBLICATIONS

You can also find my articles on my Google Scholar profile.
The symbol * represents the equal contribution.

Preprints

  1. Kangqiao Liu
    Classical codes violate the conjectured square-root bound for quantum random access codes
    [arXiv:2607.15617] (2026.07.20). PDFquan.infoSuccess-probability bounds for random access codesClassical random-access codes with private randomness already lie inside the unrestricted QRAC model and violate the conjectured square-root bound. The asymptotic worst-case boundary is instead set by Nayak’s entropy curve.
  2. Deyou Chen, Chuang Yang, and Kangqiao Liu
    Chaos bound for spinning particles in Kerr-Newman-AdS black holes
    [arXiv:2607.00432] (2026.07.01). PDFgravityChaos-bound phase maps in Kerr-Newman-AdS spacetimeNumerical phase maps locate where the particle Lyapunov exponent exceeds the black-hole surface gravity in Kerr-Newman-AdS spacetime. Rotation direction, charge, and the cosmological constant jointly reshape the violation region.
  3. Jie Gu and Kangqiao Liu
    Finite-frequency fluctuation-response bounds for open quantum systems
    [arXiv:2605.03340] (2026.05.06). PDFopen.quantstoch.thermFinite-frequency fluctuation-response bound for a cat resonatorA frequency-resolved input-output inequality bounds any measured lock-in response by fluctuations in the emitted field, with a detector-independent ceiling set by quantum Fisher information and channel activity. Driven-qubit and cat-resonator examples test the bound across frequency.

Peer-reviewed

  1. Chuang Yang, Chuanhong Gao, Deyou Chen, and Kangqiao Liu
    Bound on Lyapunov exponents with spinning particles in Kerr–Newman spacetimes
    The European Physical Journal C 86, 677 (2026) (2026.06.22) PDFgravityLyapunov exponents versus particle spin in Kerr-Newman spacetimeThe particle Lyapunov exponent can exceed the surface-gravity scale once rotation, spin, charge, or total angular momentum crosses an orientation-dependent threshold. Counter-rotating configurations and the extremal limit expose the strongest violations.
  2. Kangqiao Liu and Jie Gu
    Response kinetic uncertainty relation for Markovian open quantum systems
    Physical Review A 113, 062443 (2026) (2026.06.16)
    press release (in Chinese) by Department
    [arXiv:2501.04895]. PDFopen.quantstoch.thermActivity and intertransition sectors in the response kinetic uncertainty relationThe response kinetic uncertainty relation splits the precision cost into a jump-activity contribution and an intertransition quantum term. The two pieces dominate different perturbations, yielding a unified bound for monitored Markovian quantum dynamics.
  3. Kangqiao Liu, Masaya Nakagawa, and Masahito Ueda
    Maxwell’s demon for quantum transport
    Physical Review A 113, 022436 (2026) (2026.02.23)
    press release (in Chinese) by Department
    [arXiv:2303.08326]. PDFquan.infoMeasurement-and-feedback protocol for uphill quantum transportRepeated position measurements and feedback turn coherent hopping on a tilted lattice into directed uphill transport, storing potential energy without a thermal bias. The work quantifies the speed, power, efficiency, and robustness of this quantum information engine.
  4. Kangqiao Liu and Jie Gu
    Dynamical activity universally bounds precision of response in Markovian nonequilibrium systems
    Communications Physics 8, 62 (2025) (2025.02.11)
    see Behind the Paper for a blog post
    press release (in Chinese) by Department
    [arXiv:2410.20800]. PDFstoch.thermPerturbed and unperturbed current trajectories during relaxationFor Markovian nonequilibrium processes, response precision is universally limited by dynamical activity—the total rate of state changes—rather than dissipation alone. The bound remains valid during relaxation and can be tighter than response thermodynamic uncertainty relations.
  5. Jiale Yu*, Shiyu Wang*, Kangqiao Liu, Chen Zha, Yulin Wu, Fusheng Chen, Yangsen Ye, Shaowei Li, Qingling Zhu, Shaojun Guo, Haoran Qian, He-Liang Huang, Youwei Zhao, Chong Ying, Daojin Fan, Dachao Wu, Hong Su, Hui Deng, Hao Rong, Kaili Zhang, Sirui Cao, Jin Lin, Yu Xu, Cheng Guo, Na Li, Futian Liang, Yong-Heng Huo, Chao-Yang Lu, Cheng-Zhi Peng, Kae Nemoto, W. J. Munro, Xiaobo Zhu, Jian-Wei Pan, and Ming Gong
    Experimental demonstration of a Maxwell’s demon quantum battery in a superconducting NISQ processor
    Physical Review A 109, 062614 (2024) (2024.06.20). PDFquan.infoSuperconducting-qubit Maxwell-demon quantum-battery protocolA superconducting NISQ processor implements measurement-based Maxwell-demon feedback to steer excitations from one qubit subsystem into another. The experiment demonstrates directional charging and tracks the accompanying entropy and ergodicity dynamics.
  6. Takashi Mori, Liu Ziyin, Kangqiao Liu, and Masahito Ueda
    Power-law escape rate of SGD
    Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15959-15975, 2022 (ICML 2022) (2022.07.15)
    selected for Spotlight
    [arXiv:2105.09557]. PDFmlPower-law stationary distribution and first-passage scaling of SGDWhen SGD noise scales with the loss, its stationary distribution develops heavy power-law tails and escape from minima follows a power law rather than an Arrhenius exponential. Theory and neural-network experiments recover the predicted first-passage scaling.
  7. Liu Ziyin*, Kangqiao Liu*, Takashi Mori, and Masahito Ueda
    Strength of Minibatch Noise in SGD
    The 10th International Conference on Learning Representations (ICLR 2022) (2022.01.29)
    selected for Spotlight (5% of all submissions)
    [arXiv:2102.05375]. PDFmlFinite-learning-rate minibatch-noise theory versus experimentsA discrete-time theory gives the minibatch-noise strength and parameter fluctuations at finite learning rate, including their divergence threshold. It explains why continuous-time approximations fail at large learning rates and predicts stability across batch size, width, and regularization.
  8. Kangqiao Liu*, Liu Ziyin*, and Masahito Ueda
    Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent
    Proceedings of the 38th International Conference on Machine Learning, PMLR 139:7045-7056, 2021 (ICML 2021) (2021.07.01)
    [arXiv:2012.03636]. PDF SupplementsmlDiscrete-time and continuous-time predictions for SGD fluctuationsFinite learning rates make SGD intrinsically discrete: its stationary covariance, stability boundary, and escape efficiency differ from continuous-time Langevin predictions. The discrete theory remains accurate up to the edge of instability for both white and minibatch noise.
  9. Kangqiao Liu, Zongping Gong, and Masahito Ueda
    Thermodynamic Uncertainty Relation for Arbitrary Initial States
    Physical Review Letters 125, 140602 (2020) (2020.09.29)
    [arXiv:1912.11797]. PDF Supplementsstoch.thermTransient-current bounds for arbitrary initial statesA thermodynamic uncertainty relation valid for arbitrary initial states replaces the steady-state current by the final instantaneous current. It remains valid during transient relaxation where conventional TURs can fail and extends naturally to measurement-and-feedback protocols.