Multiconfiguration Pair-Density Functional Theory Calculations of Ground and Excited States of Complex Chemical Systems with Quantum Computers

2026-02-11

AI Summary

Accurately capturing strong electron correlation remains a central challenge in computational chemistry, particularly on near-term quantum hardware where full configuration interaction calculations are often hindered by excessively deep quantum circuits. This work proposes a hybrid approach that confines static correlation to a compact multireference state treated via the variational quantum eigensolver (VQE), while dynamic correlation is recovered classically using pair density functional theory based on reduced density matrices, with support for self-consistent orbital optimization. By innovatively separating static and dynamic correlation, this strategy substantially reduces quantum resource requirements without sacrificing physical rigor. The method achieves chemical accuracy on standard benchmarks: bond length errors of only 0.006 Å for C₂ and excitation energy errors of 0.048 eV for benzene. Moreover, it yields physically reasonable potential energy curves and dissociation behavior for the strongly correlated Cr₂ dimer (48 electrons in 42 orbitals).

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• Machine Learning: Quantum Machine Learning
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• Humans and AI: Other Foundations of Human Computation & AI

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• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
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Abstract

Accurately describing strong electron correlation in complex systems remains a prominent challenge in computational chemistry as near-term quantum algorithms treating total correlation often require prohibitively deep circuits. Here we present a hybrid strategy combining the Variational Quantum Eigensolver with Multiconfiguration Pair-Density Functional Theory to efficiently decouple correlation effects. This approach confines static correlation to a compact multireference quantum state while recovering dynamic correlation through a classical on-top density functional using reduced-density information. By enabling self-consistent orbital optimization, the method significantly reduces quantum resource overheads without sacrificing physical rigor. We demonstrate chemical accuracy on standard benchmarks by reproducing C$_2$ equilibrium bond lengths and benzene excitation energies with mean absolute errors of 0.006 {\AA} and 0.048 eV respectively. Most notably, for the strongly correlated Cr$_2$ dimer requiring a large complete active space (48e, 42o), the framework yields a bound potential-energy curve and recovers qualitative dissociation behavior despite realistic hardware noise. These results establish that separating correlation types provides a practical route to reliable predictions on near-term quantum hardware.

Problem

• strong electron correlation
• complex chemical systems
• quantum computing
• near-term quantum hardware
• computational chemistry

Innovation

• Multiconfiguration Pair-Density Functional Theory
• Variational Quantum Eigensolver
• strong electron correlation
• hybrid quantum-classical algorithm
• near-term quantum computing