Multiconfiguration Pair-Density Functional Theory Calculations of Low-lying States of Complex Chemical Systems with Quantum Computers

TL;DR

This paper introduces a hybrid quantum-classical method combining VQE and multiconfiguration pair-density functional theory to efficiently model complex chemical systems with strong electron correlation, reducing quantum resource needs.

Contribution

The authors develop a self-consistent orbital optimization approach that separates static and dynamic correlation, enabling accurate predictions on near-term quantum hardware.

Findings

Achieved chemical accuracy in bond lengths and excitation energies for benchmark molecules.

Successfully modeled the Cr2 dimer's potential energy curve with realistic hardware noise.

Reduced quantum resource requirements while maintaining physical accuracy.

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.