Exploring Quantum Computing for Metal Cluster Analysis

Area of Science:

• Computational Chemistry
• Quantum Computing
• Materials Science

Background:

• Classical computational methods face limitations in chemical accuracy and efficiency for nanoscale systems.
• Developing advanced computational workflows is crucial for accurate materials modeling.
• Quantum computing offers a potential avenue for overcoming classical limitations.

Purpose of the Study:

• To develop and implement a quantum-DFT embedding workflow for metal cluster analysis.
• To leverage quantum computing for improved electronic structure modeling.
• To assess the capabilities and limitations of near-term quantum devices in computational chemistry.

Main Methods:

• Integration of the Variational Quantum Eigensolver (VQE) with Density Functional Theory (DFT).
• Application of the quantum-DFT embedding workflow to aluminum and gold clusters.
• Testing the workflow's ability to determine electronic properties and catalytic potential.

Main Results:

• Successfully determined electronic properties for aluminum clusters up to Al7-.
• Investigated gold clusters for nitric oxide reduction potential.
• Identified challenges including memory limitations, lack of relativistic corrections, and open-shell system handling.

Conclusions:

• Quantum DFT embedding shows potential for advancing materials discovery and nanomaterial design.
• Current quantum hardware and algorithms require further development for complex chemical systems.
• This proof-of-concept study highlights the promise of quantum computing in computational chemistry.