SimuQ: A Domain-Specific Language For Quantum Simulation With Analog Compilation

ORAL

Abstract

Hamiltonian simulation is one of the most promising applications of quantum computing. Recent experimental results suggest that continuous-time analog quantum simulation would be advantageous over the gate-based digital quantum simulation in the NISQ era. However, programming such analog quantum simulators is much more challenging due to the lack of a unified interface between hardware and software and the only few known examples are all hardware-specific. We present SimuQ, the first domain-specific language for Hamiltonian simulation that supports pulse-level compilation to heterogeneous analog quantum simulators. Specifically, in SimuQ, front-end users will specify the target Hamiltonian evolution with a Hamiltonian specification language, and the programmability of analog simulators is specified through a new abstraction called the abstract analog instruction set by hardware providers. Through a solver-based compilation, SimuQ will generate the pulse-level instruction schedule on the target analog simulator for the desired Hamiltonian evolution, which has been demonstrated on pulse-controlled superconducting (Qiskit OpenPulse) and neutral-atom (Bloqade) systems, as well as on normal digital machines. We also show the advantage of the analog compilation over the digital one on IBM machines and the use of SimuQ for resource estimation for hypothetical analog machines.

*U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research Award Number DE-SC0019040, Air Force Office of Scientific Research under award number FA9550-21-1-0209, U.S. National Science Foundation grant CCF-1942837 (CAREER)

Publication: Yuxiang Peng, Jacob Young, Pengyu Liu, Xiaodi Wu. SimuQ: A Domain-Specific Language For Quantum Simulation With Analog Compilation. under submission.

Presenters

  • Yuxiang Peng

    • University of Maryland, College Park

Authors

  • Yuxiang Peng

    • University of Maryland, College Park
  • Jacob Young

    • University of Maryland, College Park
  • Pengyu Liu

    • Tsinghua University
  • Xiaodi Wu

    • University of Maryland, College Park