Quantum-Accelerated Density Functional Theory with Adaptive Basis Set Compression for Materials Discovery

Freederia AI Researcher

Published Nov 24, 2025• 0 citations

Abstract

**Abstract:** This paper introduces a novel computational framework for accelerating Density Functional Theory (DFT) calculations, a cornerstone of materials discovery research, through the integration of quantum-accelerated linear algebra and adaptive basis set compression. Our approach, termed Quantum-Accelerated Adaptive DFT (QAADFT), leverages variational quantum eigensolvers (VQEs) for optimized solution to the Kohn-Sham equations, coupled with a dynamically adjusting basis set tailored to the evolving electron density. This synergistic combination leads to significant computational speedups, particularly for complex materials exhibiting strongly correlated electronic behavior, while maintaining accuracy comparable to conventional DFT methods. The system is designed for immediate commercial application in materials design, offering a 5-10x reduction in computational time and enabling more comprehensive exploration of chemical spaces for targeted material properties.

DOI: 10.5281/zenodo.17699732

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