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

• URL: http://arxiv.org/abs/2508.10671v2
• Date: Tue, 02 Sep 2025 10:32:26 GMT
• Title: 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
• Authors: Fabio Tarocco, Pi A. B. Haase, Fabijan Pavošević, Vijay Krishna, Leonardo Guidoni, Stefan Knecht, Martina Stella,
• Abstract summary: We present a novel approach inspired by both the AVAS (Atomic Valence Active Space) and AutoCAS methods.<n>Our method unifies orbital entropy analysis with atomic orbital projections to guide the construction of chemically and physically meaningful active spaces.<n>We validate our approach on a set of molecular systems relevant to photodynamic therapy, in particular a set of Ru(II)-complexes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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