Molecular Resonance Identification in Complex Absorbing Potentials via Integrated Quantum Computing and High-Throughput Computing
Jingcheng Dai, Atharva Vidwans, Eric H. Wan, Alexander X. Miller, Micheline B. Soley
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.
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum-Dot Cellular Automata · Spectroscopy and Quantum Chemical Studies