# Molecular Resonance Identification in Complex Absorbing Potentials via Integrated Quantum Computing and High-Throughput Computing

## TL;DR

A new hybrid quantum-classical method called qDRIVE is introduced to identify molecular resonances efficiently using quantum computing and high-throughput computing.

## Contribution

The novel qDRIVE algorithm combines quantum computing with classical high-throughput computing to identify molecular resonances.

## Key findings

- qDRIVE successfully identifies resonance energies and wave functions in simulated quantum processors.
- The method leverages complex absorbing potential formalism and hybrid quantum-classical tasks.
- The approach minimizes wall time by executing tasks asynchronously and in parallel.

## Abstract

Recent advancements
in quantum algorithms have reached a state
where we can consider how to capitalize on quantum and classical computational
resources to accelerate molecular resonance state identification.
Here, we identify molecular resonances with a method that combines
quantum computing with classical high-throughput computing (HTC).
This algorithm, which we term qDRIVE (the quantum deflation resonance
identification variational eigensolver), exploits the complex absorbing
potential formalism to distill the problem of molecular resonance
identification into a network of hybrid quantum-classical variational
quantum eigensolver tasks and harnesses HTC resources to execute these
interconnected but independent tasks both asynchronously and in parallel,
a strategy that minimizes wall time to completion. We show qDRIVE
successfully identifies resonance energies and wave functions in simulated
quantum processors with current and planned specifications, which
bodes well for qDRIVE’s ultimate application in disciplines
ranging from photocatalysis to quantum control and places a spotlight
on the potential offered by integrated heterogeneous quantum computing/HTC
approaches in computational chemistry.