Demonstration of a CAFQA-bootstrapped Variational Quantum Eigensolver on a trapped-ion quantum computer

ORAL

Abstract

To enhance the variational quantum eigensolver (VQE), the CAFQA method can utilize classical computational capabilities to identify a better initial state than the Hartree-Fock method. Previous research has demonstrated that the initial state provided by CAFQA recovers more correlation energy than that of the Hartree-Fock method and results in faster convergence.

In the present study, we advance the investigation of CAFQA by demonstrating its advantages on a high-fidelity trapped-ion quantum computer located at the Duke Quantum Center---this is the first experimental demonstration of CAFQA-bootstrapped VQE on a TI device and on any academic quantum device. In our VQE experiment, we use LiH and BeH$_2$ as test cases to show that CAFQA achieves faster convergence and obtains lower energy values within the specified computational budget limits. To ensure the seamless execution of VQE on this academic device, we develop a novel hardware-software interface framework that supports independent software environments for both the circuit and hardware end. This mechanism facilitates the automation of VQE-type job executions as well as mitigates the impact of random hardware interruptions. This framework is versatile and can be applied to a variety of academic quantum devices beyond the trapped-ion quantum computer platform, with support for integration with customized packages.

*This work is supported by the ARO through the IARPA LogiQ program;the NSF QLCI program;STAQ under award NSF PHY-1818914/232580;the DOE QSA program;the AFOSR MURIs on Dissipation Engineering in Open Quantum Systems, Quantum Measurement/ Verification, and Quantum Interactive Protocols;the ARO MURI on Modular Quantum Circuits;the DOE HEP QuantISED Program;and in part by the US Department of Energy Office of Advanced Scientific Computing Research, Accelerated Research for Quantum Computing Program.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility using NERSC award NERSC DDR-ERCAP0030278.

Publication: https://arxiv.org/abs/2408.06482

Presenters

  • Qingfeng Wang

    • Tufts University

Authors

  • Qingfeng Wang

    • Tufts University
  • Liudmila Zhukas

    • Duke University
  • Qiang Miao

    • Duke University
  • Aniket S Dalvi

    • Duke University
  • Peter J Love

    • Tufts University
  • Christopher Monroe

    • Duke University
  • Frederic T Chong

    • University of Chicago
  • Gokul Subramanian Ravi

    • University of Michigan