Publications
Comment on "Spin-1/2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State"
Main points: Comment on a recent machine-learning study of the kagome Heisenberg antiferromagnet. We show that the reported low variational energies arise from broken ergodicity in Metropolis–Hastings sampling thus calling the original claims into question.
H. Kamal, D. Kufel, D. Vu, C. Laumann, N. Yao (2026)
arXiv:2605.28861 PDF Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
Main points: Using neural networks we demonstrate that exotic phases can be dynamically prepared in Rydberg quantum simulators at specific length scales, contrary to the equilibrium predictions.
D. Vu*, D. Kufel*, J. Kemp, L. Pollet, C. Laumann, N. Yao (2025)
arXiv:2512.09040 PDF Hardness of recognizing phases of matter
Main points: Proven that distinguishing quantum phases of matter is exponentially hard in correlation length. This sets limits on capabilities of any quantum AI agents for phase recognition task.
T. Schuster*, D. Kufel*, N. Yao, H. Huang (2025)
arXiv:2510.08503 PDF Spin squeezing in an ensemble of nitrogen-vacancy centers in diamond
Main points: Experimental paper on the first-to-date detection of spin squeezing in a solid state platform.
W. Wu* , E. Davis* , L. Hughes, B. Ye, Z. Wang, D. Kufel, T. Ono, S. Meynell, M. Block, C. Liu, H. Yang, A. Bleszynski-Jayich, N. Yao (2025) Field: AI & Condensed Matter
Nature 646, p. 74–80 arXiv:2503.14585 PDF Approximately-symmetric neural networks for quantum spin liquids
Main points: Constructed tailor-made, scalable and interpretable neural network architectures for studying quantum spin liquid problems.
D. Kufel*, J. Kemp*, D. Vuy, S. Linsel, C. Laumann, N. Yao (2025) Field: Classical Mechanics
Physical Review Letters 135, 056702 (Editor's Suggestion) arXiv:2405.17541 PDF Skier and loop the loop with friction
Main points: Developed analytical solutions to the extension of two ‘classic’ problems in classical mechanics.
D. Kufel, A. Sokal (2022) Field: Atomic Physics
American Journal of Physics 90, 573 arXiv:2003.02178 PDF Alternative quantisation condition for wavepacket dynamics in a hyperbolic double well
Main points: Proposed a new analytical way of finding allowed energies in the class of hyperbolic-double well potentials by connecting it to a problem of finding roots of some polynomial. Applied this approach to understanding the role of non-adiabatic effects during enhanced ionization.
D. Kufel, H. Chomet, C. Faria (2021) Field: AI
Journal of Physics A: Mathematical and Theoretical 54, 035304 arXiv:2009.08737 PDF Online Learning and matching for resource allocation problems
Main points: Devised, provided performance guarantees, and implemented algorithms integrating dual problems in convex optimization with a subclass of reinforcement learning techniques. Applied these algorithms to the traffic-shaping problem.
A. Boskovic, Q. Chen, D. Kufel, Z. Zhou (2019) Field: Computational Neuroscience
SIAM SIURO vol. 13 arXiv:1911.07409 PDF Analytical modelling of temperature effects on AMPA-type synapse
Main Points: Used ODE-based modelling for understanding temperature effects on AMPA-type synapses in brain. Simplified the ODEs using some physically-motivated assumptions and shown how the obtained analytical solution faithfully reproduces the results of biological experiments.
D. Kufel, G. Wojcik (2018)
Journal of Computational Neuroscience 44, 379-391 arXiv:1610.00611 PDF
Refereeing
I refereed for Physical Review Letters [multiple times], International Conference on Machine Learning (ICML) - AI4Science Workshop, Physical Review A, Quantum Information Processing conference (QIP), and Quantum Computing Theory in Practice (QCTIP).