8 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…
Masked Diffusion Modeling for Anomaly Detection
Lixing Zhang, Yuchen Liang, Liyan Xie
Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. However, developing effective anomal…
ParamSpMM: Adaptive and Efficient Sparse Matrix-Matrix Multiplication on GPUs for GNNs
Lixing Zhang, Guanhua Ye, Hongzheng Li +2
Fueled by the ability to mine real-world graph data, GNN applications have experienced phenomenal growth. Sparse Matrix-Matrix Multiplication (SpMM) is a critical operator in GNNs.…
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…
Sequential Change Detection for Multiple Data Streams with Differential Privacy
Lixing Zhang, Liyan Xie, Ruizhi Zhang
Sequential change-point detection seeks to rapidly identify distributional changes in streaming data while controlling false alarms. Existing multi-stream detection methods typical…
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…
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…