Q-CHOP proposes a new quantum algorithm for constrained optimization problems, addressing challenges faced by classical and quantum optimization algorithms due to constraints. By enforcing a Hamiltonian constraint and slowly transitioning from the worst feasible state to the best feasible state, Q-CHOP shows superior performance compared to traditional methods. The study explores applications in combinatorial optimization problems like graphs, knapsack, and financial use cases.
The content discusses the ubiquity of constrained combinatorial optimization problems in science and industry, emphasizing the potential of quantum computers to revolutionize their solution. Various quantum algorithms are reviewed for constrained optimization, highlighting the advantages of adiabatic quantum computation models. Q-CHOP is introduced as a promising approach that enforces constraints throughout adiabatic evolution.
Furthermore, the study delves into specific strategies employed by Q-CHOP for different types of objectives and constraints. It addresses challenges related to inequality constraints and provides insights into optimizing complex problems like knapsack and combinatorial auctions using quantum computing techniques. Performance comparisons between Q-CHOP and traditional methods reveal significant improvements in approximation ratios and optimal state probabilities across various problem instances.
Overall, the content showcases how Q-CHOP offers a unique perspective on solving constrained optimization problems efficiently using quantum computing principles.
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by Michael A. P... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.05653.pdfDeeper Inquiries