Noise-Resilient Quantum Chemistry with Half the Qubits

TL;DR

This paper introduces HSQD, a half-qubit quantum diagonalization method that reduces qubit requirements and noise, enabling accurate quantum chemistry simulations on NISQ devices with fewer resources.

Contribution

The paper presents HSQD, a novel half-qubit SQD approach that halves qubit use and circuit complexity, improving noise resilience and efficiency in quantum chemistry simulations.

Findings

HSQD matches SQD accuracy with half the qubits on IBM hardware.

HCI-HSQD achieves sub-millihartree accuracy across N2 potential energy surface.

HCI-HSQD reduces qubit requirements and measurement costs for large iron-sulfur clusters.

Abstract

Sample-based quantum diagonalization (SQD) offers a powerful route to accurate quantum chemistry on noisy intermediate-scale quantum (NISQ) devices by combining quantum sampling with classical diagonalization. Here we introduce HSQD, a novel half-qubit SQD approach that halves the qubit requirement for simulating a chemical system and drastically reduces overall circuit depth and gate counts, suppressing hardware noise. When modeling the dissociation of the nitrogen molecule with a (10e, 26o) active space, HSQD matches the accuracy of SQD on IBM quantum hardware using only half the number of qubits and 40% fewer measurements. We further enhance HSQD with a heat-bath configuration interaction (HCI) inspired selection of the samples, forming HCI-HSQD. This yields sub-millihartree accuracy across the N2 potential energy surface and produces subspaces up to 39% smaller than those from classical HCI, showing a significant improvement in the compactness of the ground-state representation. Finally, we demonstrate the scalability of HCI-HSQD using iron-sulfur clusters, reaching active spaces of up to (54e, 36o) while using only half as many qubits as the original SQD. For these systems, HCI-HSQD reduces SQD energy errors by up to 76% for [2Fe-2S] and 26% for [4Fe-4S], while also reducing subspace sizes, halving measurement requirements, and eliminating expensive post-processing. Together, these results establish half-qubit SQD as a noise-resilient and resource-efficient pathway toward practical quantum advantage in strongly correlated chemistry.