Fermion sampling made more efficient
Haoran Sun, Jie Zou, and Xiaopeng Li
Phys. Rev. B 107, 035119 (2023) - Published 11 January, 2023
Fermion sampling is to generate probability distribution of a many-body Slater-determinant wave function, which is termed “determinantal point process” in statistical analysis. For its inherently embedded Pauli exclusion principle, its application reaches beyond simulating fermionic quantum many-body physics to constructing machine learning models for diversified datasets. Here we propose a fermion sampling algorithm, which has a polynomial time complexity—quadratic in the fermion number and linear in the system size. This algorithm is about more efficient in computation time than the best known algorithms. In sampling the corresponding marginal distribution, our algorithm has a more drastic improvement, achieving a scaling advantage. We demonstrate its power on several test applications, including sampling fermions in a many-body system and a machine learning task of text summarization, and confirm its improved computation efficiency over other methods by counting floating-point operations.

