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Quantum Optimization
Quantum Machine Learning
Constraint-oriented biased quantum search for linear constrained combinatorial optimization problems
arXiv
Authors: Sören Wilkening, Timo Ziegler, Maximilian Hess
Year
2025
Paper ID
16161
Status
Preprint
Abstract Read
~2 min
Abstract Words
62
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- In this paper, we extend a previously presented Grover-based heuristic to tackle general combinatorial optimization problems with linear constraints.
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