Sample-based quantum diagonalization as parallel fragment solver for the localized active space self-consistent field method
Qiaohong Wang, Mario Motta, Ruhee D'Cunha, Kevin J. Sung, Matthew R. Hermes, Tanvi Gujarati, Yukio Kawashima, Yu-ya Ohnishi, Gavin O. Jones, Laura Gagliardi

TL;DR
This paper introduces LASSQD, a quantum-assisted method that uses sample-based quantum diagonalization to efficiently solve fragment Schrödinger equations in the LASSCF framework, enabling accurate treatment of strongly correlated systems.
Contribution
The paper presents LASSQD, a novel hybrid quantum-classical approach that overcomes computational bottlenecks in LASSCF by employing quantum sampling for fragment solutions.
Findings
LASSQD can handle larger fragment sizes than traditional LASSCF.
LASSQD achieves energy accuracy within 1 kcal/mol of LASSCF.
The method is competitive with classical quantum chemistry approaches.
Abstract
Accurately and efficiently describing strongly correlated electronic systems is a central challenge in quantum computational chemistry, with classical and quantum computers. The localized active space self-consistent field method (LASSCF) uses a product of fragment active spaces as a variational space, with the Schr\"odinger equation solved exactly in each fragment and the fragment active-space orbitals defined in a self-consistent manner. LASSCF is accurate for systems with strong intra-fragment and weak inter-fragment correlation, and its computational cost is combinatorial with respect to the size of the individual fragment active spaces, rather than their product. However, exactly solving the Schr\"odinger equation in each fragment remains a substantial bottleneck. Here, we address the possibility of solving the fragment active space Schr\"odinger equation with approximate methods,…
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Taxonomy
TopicsMagnetism in coordination complexes · Machine Learning in Materials Science · Quantum Computing Algorithms and Architecture
