SOLAX: A Python solver for fermionic quantum systems with neural network support

TL;DR

SOLAX is a Python library that combines quantum many-body modeling with machine learning to efficiently simulate fermionic systems, aiding research in physics and chemistry.

Contribution

It introduces a modular, machine learning-enabled Python framework for simulating fermionic quantum systems using second quantization.

Findings

Successfully applied to the Single Impurity Anderson Model

Provides a flexible tool for large quantum cluster simulations

Integrates JAX for high-performance computations

Abstract

Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.