10 papers
Out-of-distribution Neural Inference in Dynamical Ising Models
Yuan-Bin Zhu, Shuang Qiao, Shi-Ju Ran
Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects…
A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models
Ying Lu, Peng-Fei Zhou, Qi-Xuan Fang +3
Dense linear maps carry much of the parameter and computational burden of modern neural networks, yet their dense form leaves the organization of learned couplings implicit. Quantu…
Geometric Prototype Learning in Quantum Hilbert Space with Matrix Product States
Kun Zhang, Lei Ding, Sheng-Chen Bai +4
Quantum probability provides a novel framework for formulating machine-learning (ML) problems in Hilbert space. We introduce a prototype-based learning scheme where class represent…
Statistics-encoded tensor network approach in disordered quantum many-body spin chains
Hao Zhu, Ding-Zu Wang, Shi-Ju Ran +1
Simulating the dynamics of quantum many-body systems with disorder is a fundamental challenge. In this work, we propose a general approach -- the statistics-encoded tensor network…
Matrix-product entanglement characterizing the optimality of state-preparation quantum circuits
Shuo Qi, Wen-Jun Li, Gang Su +1
Multipartite entanglement offers a powerful framework for understanding the complex collective phenomena in quantum many-body systems that are often beyond the description of conve…
Tensor-network variational diagonalization of quantum many-body spectra
Peng-Fei Zhou, Shuang Qiao, An-Chun Ji +1
Complete many-body spectra encode thermodynamics, dynamical response, and quantum chaos, yet their exponential size places them beyond enumeration. We introduce tensor-network vari…