Antisymmetric Function

Antisymmetric functions, crucial in quantum physics for representing fermionic wavefunctions, are the focus of intense research aimed at developing efficient approximation methods. Current efforts concentrate on comparing and improving the performance of different model architectures, such as Slater determinants and Jastrow ansatzes, which combine determinants with symmetric functions, and exploring the use of neural networks with tailored antisymmetrization techniques to mitigate computational challenges. These advancements are vital for improving the accuracy and scalability of simulations in quantum chemistry and related fields, enabling more precise modeling of complex many-body systems.

Papers

March 2, 2024

Uniform $\mathcal{C}^k$ Approximation of $G$-Invariant and Antisymmetric Functions, Embedding Dimensions, and Polynomial Representations
Soumya Ganguly, Khoa Tran, Rahul Sarkar
Box Dimension Symmetric Function Antisymmetric Function

March 22, 2023

Anti-symmetric Barron functions and their approximation with sums of determinants
Nilin Abrahamsen, Lin Lin
High Dimensional Average Approximation Social Determinant Partial Sum Ab Initio Quantum Chemistry Barron Space Barron Function Antisymmetric Function

August 5, 2022

Towards Antisymmetric Neural Ansatz Separation
Aaron Zweig, Joan Bruna
Log Determinant Antisymmetric Function

May 24, 2022

Efficient anti-symmetrization of a neural network layer by taming the sign problem
Nilin Abrahamsen, Lin Lin
Cultural Sign Neural Network Layer Symmetric Neural Network Learning Probabilistic Symmetrization Antisymmetric Function

Antisymmetric Function

Papers

Uniform $\mathcal{C}^k$ Approximation of $G$-Invariant and Antisymmetric Functions, Embedding Dimensions, and Polynomial Representations

Anti-symmetric Barron functions and their approximation with sums of determinants

Towards Antisymmetric Neural Ansatz Separation

Efficient anti-symmetrization of a neural network layer by taming the sign problem