HATT: Hamiltonian Aware Ternary Tree for Optimizing Fermion-to-Qubit Mapping

ORAL

Abstract

This paper introduces the Hamiltonian-Aware Ternary Tree (HATT) framework to compile optimized Fermion-to-qubit mapping for specific Fermionic Hamiltonians. In the simulation of Fermionic quantum systems, efficient Fermion-to-qubit mapping plays a critical role in transforming the Fermionic system into a qubit system. HATT utilizes ternary tree mapping and a bottom-up construction procedure to generate Hamiltonian aware Fermion-to-qubit mapping to reduce the Pauli weight of the qubit Hamiltonian, resulting in lower quantum simulation circuit overhead. Additionally, our optimizations retain the important vacuum state preservation property in our Fermion-to-qubit mapping and reduce the complexity of our algorithm from $O(N^4)$ to $O(N^3)$. Evaluations and simulations of various Fermionic systems demonstrate a significant reduction in both Pauli weight and circuit complexity, alongside excellent scalability to larger systems. Experiments on the Ionq quantum computer also show the advantages of our approach in noise resistance in quantum simulations.

*NSF CAREER Award CCF-2338773ExpandQISE Award OSI-2427020Amazon Web Service Cloud CreditThis work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Contract No.DE-AC02-05CH11231, through the MACH-Q project in the Accelerated Research in Quantum Computing Program.

Publication: https://dl.acm.org/doi/10.1145/3620666.3651371
https://arxiv.org/abs/2409.02010v1

Presenters

  • Yuhao Liu

    • University of Pennsylvania

Authors

  • Yuhao Liu

    • University of Pennsylvania
  • Kevin Yao

    • University of Pennsylvania
  • Jonathan Hong

    • University of Pennsylvania
  • Julien Froustey

    • University of California, Berkeley
  • Yunong Shi

    • Amazon.com, Inc.
  • Ermal Rrapaj

    • University of California, Berkeley
    • Lawrence Berkeley National Laboratory
  • Costin C Iancu

    • Lawrence Berkeley National Laboratory
  • Gushu Li

    • University of Pennsylvania