Exploring Accurate Potential Energy Surfaces via Integrating Variational Quantum Eigensolver with Machine Learning.

Area of Science:

• Quantum computing applications in chemistry
• Computational chemistry and molecular modeling

Background:

• Potential energy surfaces (PESs) are vital for understanding chemical reactions.
• Accurate PES prediction using high-level electronic structure methods is computationally expensive.
• Variational quantum algorithms (VQEs) offer a quantum computing approach to PES calculation.

Purpose of the Study:

• To develop an efficient scheme for accurate potential energy surface (PES) exploration.
• To integrate variational quantum algorithms with machine learning techniques.
• To overcome the computational cost limitations of traditional PES prediction methods.

Main Methods:

• Encoding molecular geometry into a deep neural network (DNN) to parameterize the variational quantum eigensolver (VQE).
• Utilizing the wave function ansatz to represent the PES.
• Training the DNN model to avoid computationally intensive variational optimization procedures.

Main Results:

• The proposed hybrid quantum-classical approach significantly accelerates PES evaluation.
• A simple DNN model successfully reproduced accurate PESs for small molecules.
• The method bypasses the need for iterative variational optimization in VQE calculations.

Conclusions:

• The integration of DNNs with VQE presents a promising strategy for efficient and accurate PES exploration.
• This approach accelerates the computation of PESs, making them more accessible for complex chemical systems.
• The findings highlight the potential of quantum computing and machine learning synergy in computational chemistry.