12 citations · 15 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…