Author
Listed:
- Samuel Kushnir
(Department of Computer Science, University of Maryland, College Park, Maryland 20742)
- Jiaqi Leng
(Department of Mathematics, University of Maryland, College Park, Maryland 20742; and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742; and Department of Mathematics and Simons Institute for the Theory of Computing, University of California, Berkeley, California 94720)
- Yuxiang Peng
(Department of Computer Science, University of Maryland, College Park, Maryland 20742; and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742)
- Lei Fan
(Department of Engineering Technology, University of Houston, Houston, Texas 77204; and Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77204)
- Xiaodi Wu
(Department of Computer Science, University of Maryland, College Park, Maryland 20742; and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742)
Abstract
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 .
Suggested Citation
Samuel Kushnir & Jiaqi Leng & Yuxiang Peng & Lei Fan & Xiaodi Wu, 2025.
"QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent,"
INFORMS Journal on Computing, INFORMS, vol. 37(1), pages 107-124, January.
Handle:
RePEc:inm:orijoc:v:37:y:2025:i:1:p:107-124
DOI: 10.1287/ijoc.2024.0587
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