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Circuit-Depth Reduction of Unitary-Coupled-Cluster Ansatz by Energy Sorting.

Yi FanChang-Su CaoXusheng XuZhenyu LiDingshun LvMan-Hong Yung
Published in: The journal of physical chemistry letters (2023)
Quantum computation represents a revolutionary approach to solving problems in quantum chemistry. However, due to the limited quantum resources in the current noisy intermediate-scale quantum (NISQ) devices, quantum algorithms for large chemical systems remain a major task. In this work, we demonstrate that the circuit depth of the unitary coupled cluster (UCC) and UCC-based ansatzes in the algorithm of the variational quantum eigensolver can be significantly reduced by an energy-sorting strategy. Specifically, subsets of excitation operators are first prescreened from the operator pool according to its contribution to the total energy. The quantum circuit ansatz is then iteratively constructed until convergence of the final energy to a typical accuracy. For demonstration, this method has been successfully applied to molecular and periodic systems. Particularly, a reduction of 50%-98% in the number of operators is observed while retaining the accuracy of the original UCCSD operator pools. This method can be straightforwardly extended to general parametric variational ansatzes.
Keyphrases
  • molecular dynamics
  • energy transfer
  • machine learning
  • monte carlo
  • optical coherence tomography
  • single molecule