QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent

We develop an open-source, end-to-end software (named QHDOPT), which can solve nonlinear optimization problems using the quantum Hamiltonian descent (QHD) algorithm. QHDOPT offers an accessible interface and automatically maps tasks to various supported quantum backends (i.e., quantum hardware machines). These features enable users, even those without prior knowledge or experience in quantum computing, to utilize the power of existing quantum devices for nonlinear and nonconvex optimization tasks. In its intermediate compilation layer, QHDOPT employs SimuQ, an efficient interface for Hamiltonian-oriented programming, to facilitate multiple algorithmic specifications and ensure compatible cross-hardware deployment. The detailed documentation of QHDOPT is available at https://github.com/jiaqileng/QHDOPT . History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the U.S. Department of Energy’s Advanced Research Projects Agency–Energy [Grant DE-SC0020273], the Alfred P. Sloan Foundation, the Simons Foundation [Simons Investigator Award 825053], the Simons Quantum Postdoctoral Fellowship, the National Science Foundation [Grants CCF-1816695, CCF-1942837, and ECCS-2045978], the Unitary Fund, and the Air Force Office of Scientific Research [Grant FA95502110051]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0587 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0587 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .