AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications

TL;DR

This paper introduces AEGISS, a semi-automated, unified active space selection workflow that combines orbital entropy and atomic orbital projections to improve reliability, scalability, and applicability in quantum chemistry and quantum computing.

Contribution

The novel AEGISS method unifies entropy analysis with atomic orbital projections for automated, flexible active space selection across diverse molecular systems.

Findings

Reliable identification of chemically meaningful active spaces

Effective in systems with strong static correlation

Compatible with classical and quantum computational frameworks

Abstract

The selection of a balanced active space is a critical step in multi-reference quantum chemistry calculations, particularly for systems with strong electron correlation. Likewise, active space selection is a key to unlock the potential of contemporary quantum computing in quantum chemistry. Albeit recent progress, there remains a lack of a unified, robust, and fully automated framework for active space selection that performs reliably across a wide range of molecular systems. In this work, we present a novel approach inspired by both the AVAS (Atomic Valence Active Space) and AutoCAS methods. Our method unifies orbital entropy analysis with atomic orbital projections to guide the construction of chemically and physically meaningful active spaces. This integrated scheme enables a more consistent and flexible selection of active orbitals while retaining automation and scalability. We validate our approach on a set of molecular systems relevant to photodynamic therapy, in particular a set of Ru(II)-complexes, selected to span increasing levels of electron correlation and structural complexity. These molecules serve as challenging test cases due to the presence of strong static correlation and the need for highly accurate electronic structure descriptions. Our results demonstrate that the method can reliably identify compact, chemically intuitive active spaces that capture the essential physics, making it suitable for both classical and quantum computational frameworks. Furthermore, we have developed this approach in a package that is intuitive to use for users and can be interfaced with both standard quantum chemistry and quantum computing applications, making it accessible to a broad research community.