10 papers
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…
Continuous Variable Hamiltonian Learning at Heisenberg Limit via Displacement-Random Unitary Transformation
Xi Huang, Lixing Zhang, Di Luo
Characterizing continuous-variable (CV) Hamiltonians can be formulated as Hamiltonian learning under quantum measurement constraints: finite operator coefficients are inferred from…
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…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
A universal compression theory for lottery ticket hypothesis and neural scaling laws
Hong-Yi Wang, Di Luo, Tomaso Poggio +2
When training large-scale models, the performance typically scales with the number of parameters and the dataset size according to a slow power law. A fundamental theoretical and p…
CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers
Haining Pan, James V. Roggeveen, Erez Berg +16
Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce.…