12 citations · 15 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
Chiral Cavity Control of the Interlayer Exciton Energy Spectrum
Jonathan Sanchez-Lopez, Ze-Xun Lin, Di Luo +1
Heterostructures of two-dimensional materials offer a versatile platform to study light-matter interactions of electron and hole gases. By separating electron and hole layers with…
Neural Transformer Backflow for Solving Momentum-Resolved Ground States of Strongly Correlated Materials
Lixing Zhang, Di Luo
Strongly correlated materials host a rich variety of exotic quantum phases but remain challenging to solve due to strong interactions. We introduce the Neural Transformer Backflow…
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…
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.…
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…
Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
Lucas Tecot, Di Luo, Cho-Jui Hsieh
Advancements in quantum computing have spurred significant interest in harnessing its potential for speedups over classical systems. However, noise remains a major obstacle to achi…
Hamiltonian Learning at Heisenberg Limit for Hybrid Quantum Systems
Lixing Zhang, Ze-Xun Lin, Prineha Narang +1
Hybrid quantum systems with different particle species are fundamental in quantum materials and quantum information science. In this work, we establish a rigorous theoretical frame…
Operator Learning Renormalization Group
Xiu-Zhe Luo, Di Luo, Roger G. Melko
In this paper, we present a general framework for quantum many-body simulations called the operator learning renormalization group (OLRG). Inspired by machine learning perspectives…
Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
Joshua Lin, Di Luo, Xiaojun Yao +1
Ab-initio simulations of multiple heavy quarks propagating in a Quark-Gluon Plasma are computationally difficult to perform due to the large dimension of the space of density matri…