A hybrid data-driven quantum approach to expedite molecular wavefunction calculations

Recent studies in quantum computing have demonstrated that the localized unitary Jastrow (LUCJ) ansatz, a promising electronic structure method capable of running on near-term quantum hardware, can surpass exact solutions on classical hardware by using quantum-centric supercomputing. However, scalable and efficient methods for initializing the LUCJ ansatz remain underexplored in current research. We propose a hybrid classical-quantum algorithm that incorporates a data-driven coupled-cluster (DDCC) approach to initialize the LUCJ ansatz, with the aim of significantly reducing computational overhead while preserving chemical accuracy. This approach leverages the speed of machine learning to expedite molecular wavefunction calculations for larger molecular systems, making it a viable candidate for near-term quantum advantage in electronic structure problems.

Faculty Supervisor:

Hans-Arno Jacobsen

Student:

Partner:

University of Exeter

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: