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cond-mat.str-el2026
Scaling Theory for Learning Low-Energy Quantum Subspaces
Guijing Duan, Chen Mo, Di Luo
Phase diagrams and energy surfaces require low-energy states across a family of Hamiltonians, but solving each parameter point independently is prohibitively expensive. An importan…
quant-ph2026
Foundation Neural Effective Hamiltonian for Strongly Correlated Quantum Materials
Lixing Zhang, Hongjie Jiang, Di Luo
Simulating strongly correlated quantum materials often involves not a single Hamiltonian, but a family of Hamiltonians whose ground states evolve across experimentally tunable coup…
physics.comp-ph2026
WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws
Lixing Zhang, Guijing Duan, Di Luo
We present a comprehensive benchmarking dataset and empirical scaling law analysis for neural network wavefunctions by matching them to a wide spectrum of famous many body target w…