Solving an Industrially Relevant Quantum Chemistry Problem on Quantum Hardware

TL;DR

This paper demonstrates a quantum computing approach to accurately calculate properties of industrially relevant, strongly correlated chemical systems using variational algorithms on trapped ion hardware, integrating results into chemical workflows.

Contribution

It introduces a measurement-efficient variational quantum algorithm for strongly correlated chemical systems on hardware, achieving chemical accuracy and practical integration into workflows.

Findings

Achieved chemical accuracy with quantum hardware calculations.

Implemented a measurement-efficient variational algorithm.

Successfully integrated quantum results into industrial chemical workflows.

Abstract

Quantum chemical calculations are among the most promising applications for quantum computing. Implementations of dedicated quantum algorithms on available quantum hardware were so far, however, mostly limited to comparatively simple systems without strong correlations. As such, they can also be addressed by classically efficient single-reference methods. In this work, we calculate the lowest energy eigenvalue of active space Hamiltonians of industrially relevant and strongly correlated metal chelates on trapped ion quantum hardware, and integrate the results into a typical industrial quantum chemical workflow to arrive at chemically meaningful properties. We are able to achieve chemical accuracy by training a variational quantum algorithm on quantum hardware, followed by a classical diagonalization in the subspace of states measured as outputs of the quantum circuit. This approach is particularly measurement-efficient, requiring 600 single-shot measurements per cost function evaluation on a ten qubit system, and allows for efficient post-processing to handle erroneous runs.