Constraint-oriented biased quantum search for linear constrained combinatorial optimization problems
Abstract
In this paper, we extend a previously presented Grover-based heuristic to tackle general combinatorial optimization problems with linear constraints. We further describe the introduced method as a framework that enables performance improvements through circuit optimization and machine learning techniques. Comparisons with state-of-the-art classical solvers further demonstrate the algorithm's potential to achieve a quantum advantage in terms of speed, given appropriate quantum hardware.
0
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.