Accelerating variational quantum eigensolver convergence using parameter transfer

Artikel i vetenskaplig tidskrift, 2023

One impediment to the useful application of variational quantum algorithms in quantum chemistry is slow convergence with large numbers of classical optimization parameters. In this work, we evaluate a quantum computational warm-start approach for potential energy surface calculations. Our approach, which is inspired by conventional computational methods, is evaluated using simulations of the variational quantum eigensolver. Significant speedup is demonstrated relative to calculations that rely on a Hartree-Fock initial state, both for ideal and sampled simulations. The general approach of transferring parameters between similar problems is promising for accelerating current and near-term quantum chemistry calculations on quantum hardware, and is likely applicable beyond the tested algorithm and use case.