Range-separated density functional theory using multiresolution analysis and quantum computing.

Area of Science:

• Quantum Computing
• Computational Chemistry
• Quantum Chemistry

Background:

• Quantum computers promise significant advantages over classical computers for complex quantum chemistry problems.
• Current quantum chemistry calculations are computationally expensive, limiting their application.
• Partitioning molecular systems is a key strategy to reduce computational costs.

Purpose of the Study:

• To investigate a novel approach for reducing the computational resources required for quantum chemistry simulations.
• To explore the effectiveness of range-separated density functional theory (RS-DFT) in partitioning molecular systems.
• To assess the impact of using pair natural orbitals (PNOs) on the efficiency of quantum algorithms.

Main Methods:

• Molecular systems were partitioned using range-separated density functional theory (RS-DFT).
• Pair natural orbitals (PNOs) were generated using a basis-set independent multiresolution analysis (MRA) framework.
• The variational quantum eigensolver (VQE) algorithm was employed to test the strategy on various molecules.

Main Results:

• RS-DFT reduced the dependence of ground state energy on basis set and active space size compared to wave function theory (WFT).
• Utilizing PNOs led to more compact qubit Hamiltonians.
• The proposed approach significantly reduced the number of qubits required to achieve a target energy accuracy.

Conclusions:

• The combination of RS-DFT and PNOs offers an efficient strategy for quantum chemistry calculations.
• This method lowers the qubit requirements for accurate energy calculations, making quantum simulations more feasible.
• The findings pave the way for more accessible and scalable quantum simulations in chemistry.