Variational Monte Carlo

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11 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo
Oct 9, 2026Qiao Gao, Yang Huang, Enze Hou +3Variational Monte Carlo Computational Quantum Chemistry
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · University of Science and Technology of China, Hefei 230026, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215213, China +5
Differentiable Systematic Resampling for Variational Sequential Monte Carlo
Oct 8, 2026Fredrik Cumlin, Saikat ChatterjeeVariational Monte Carlo Particle Filtering
Department of Information Science, KTH Royal Institute of Technology, Stockholm, Sweden
Apparent Compression, Real Stability: The Intrinsic Dimension of Learning a Quantum Wavefunction
Sep 27, 2026Lu Wei, Yufeng Wang, Chenfeng Cao +1Variational Monte Carlo Intrinsic Dimensionality
Data Science Department, Stony Brook University · Department of Computer Science, Stony Brook University · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin +1
Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization
Jun 24, 2026Philipp Grohs, Davide NobileStochastic Optimization Variational Monte Carlo
Faculty of Mathematics, University of Vienna · RICAM, Austrian Academy of Sciences
Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Jun 11, 2026Yi-Ran Xue, Rui Wang, Baigeng Wang +1Neural Quantum States Variational Monte Carlo
*National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, China. · Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA. · Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China. +3
Comment on "Spin-1/2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State"
May 21, 2026Helia Kamal, Dominik Kufel, DinhDuy Vu +2Neural Quantum States Markov Chain Monte Carlo
Department of Physics, Harvard University, Cambridge, MA 02138, USA · Harvard Quantum Initiative, Cambridge, MA 02138, USA · Department of Physics, Boston University, Boston, MA 02215, USA
Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
May 13, 2026Ejaaz Merali, Mohamed Hibat-Allah, Mohammad Kohandel +2Neural Quantum States Variational Monte Carlo
Department of Physics and Astronomy, University of California, Davis, California 95616, USA · Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada · Vector Institute, Toronto, Ontario, M5G 0C6, Canada +1
Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
Mar 26, 2026P. Bernát Szabó, Zeno Schätzle, Frank NoéVariational Monte Carlo Quantum Chemistry
FU Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany · Microsoft Research AI4Science, Karl-Liebknecht Str. 32, 10178 Berlin, Germany · FU Berlin, Department of Physics, Arnimallee 14, 14195 Berlin, Germany +1
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Mar 15, 2026Nicholas Gao, Till Grutschus, Frank Noé +1Variational Monte Carlo Neural Quantum States
Department of Computer Science & Munich Data Science Institute, Technical University of Munich · CuspAI · Free University of Berlin +2
MDP Planning as Policy Inference
Feb 19, 2026David TolpinVariational Monte Carlo Markov Decision Processes
Offtopia
Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study
Dec 19, 2025Shengdu Chai, Chen Lin, Xinyang Dong +4Variational Monte Carlo Crystal Structure Prediction
Interdisciplinary Center for Theoretical Physics and Information Sciences (ICTPIS), Fudan University, Shanghai 200433, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China · Department of Engineering, University of Oxford, Oxford OX1 4BH, UK +4

Oct 9, 2026J/K move · Enter open · S save

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

Qiao Gao, Yang Huang, Enze Hou, Yuzhi Liu+2

School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · University of Science and Technology of China, Hefei 230026, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215213, China+5

Variational Monte Carlo Computational Quantum Chemistry Neural Quantum States Quantum Chemistry

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Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schrödinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring separate calculations to map out a potential energy surface. We introduce NucleoNet, a nuclear-aware determinant ansatz in which electrons and nuclei form a unified particle set propagated through a shared electron--nucleus backbone that jointly evolves one-particle and two-particle representations. Nuclei carry learned representations that evolve alongside the electronic representations and condition orbital construction, while antisymmetry is imposed only by electronic determinants. On equilibrium small-molecule benchmarks, NucleoNet achieves accuracy comparable to established neural-wavefunction baselines with substantially fewer parameters. For each molecular system, a single model optimized jointly across geometries describes the N

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dissociation curve, Li displacement in LiCN, and the ethane and formamide minimum-energy paths for bond dissociation. Sparse-geometry training generalizes to unseen geometries for N

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and LiCN without further optimization, and N

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ablations show that nuclear streams substantially improve this interpolation. These results show that a compact wavefunction with jointly evolved nuclear representations can describe the geometries of a molecular system with a single set of parameters.