Multistate iterative qubit coupled cluster (MS-iQCC): a quantum-inspired, state-averaged approach to ground- and excited-state energies

TL;DR

The MS-iQCC method is a quantum-inspired, state-averaged algorithm that efficiently predicts both ground and excited states of molecules on classical hardware, addressing challenges of strong correlation and computational cost.

Contribution

It introduces a multistate, state-averaged approach to qubit coupled cluster that improves excited-state predictions and supports multireference electronic structure calculations.

Findings

Robust convergence of state energies to chemical accuracy.

Effective handling of strongly correlated geometries.

Applicable to diverse molecules like H4, H2O, N2, and C2.

Abstract

We introduce the multistate iterative qubit coupled cluster (MS-iQCC) method, a quantum-inspired algorithm that runs efficiently on classical hardware and is designed to predict both ground and excited electronic states of molecules. Accurate excited-state energetics are essential for interpreting spectroscopy and chemical reactivity, but standard electronic structure methods are either too computationally expensive for larger systems or lose reliability in the presence of strong electron correlation. MS-iQCC addresses this challenge by simultaneously optimizing multiple electronic states in a single, state-averaged procedure that treats ground and excited states on equal footing. This removes the energetic bias that is introduced when excited states are computed one at a time and constrained to remain orthogonal to previously optimized states. The approach supports multireference electronic structure by allowing multideterminantal initial guesses and by adaptively building a compact exponential ansatz from a pool of qubit excitation generators. We apply MS-iQCC to H$_4$, H$_2$O, N$_2$, and C$_2$, including strongly correlated geometries, and observe robust convergence of all targeted state energies to chemically meaningful accuracy across their potential energy surfaces.