IMPLEMENTATION OF A DENSITY FUNCTIONAL THEORY ON QUANTUM PROCESSORS BY GENERALIZED GRADIENT APPROACH
Patent Information
- Authority / Receiving Office
- DE · DE
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2026-04-15
AI Technical Summary
Current classical computational methods for simulating large chemical systems face significant bottlenecks due to cubic and quartic scaling in Kohn-Sham Density Functional Theory (DFT) calculations, leading to lengthy computation times, especially for systems with thousands of atoms, which are not adequately addressed by existing hybrid quantum-classical approaches.
Implementing Density Functional Theory (DFT) on quantum processors using generalized gradient approximation, utilizing quantum processors to solve eigenvalue problems through a hybrid quantum computing approach, which includes a method for implementing density functional theory on a quantum processor.
This approach significantly reduces the computational time and complexity of DFT calculations, enabling efficient simulation of large chemical systems by leveraging quantum processors to overcome the scaling limitations of classical methods.