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Quantum Optimization
Quantum Machine Learning
Approximation algorithms for noncommutative CSPs
arXiv
Authors: Eric Culf, Hamoon Mousavi, Taro Spirig
Year
2023
Paper ID
53012
Status
Preprint
Abstract Read
~2 min
Abstract Words
79
Citations
N/A
Abstract
Noncommutative constraint satisfaction problems (NC-CSPs) are higher-dimensional operator extensions of classical CSPs. Despite their significance in quantum information, their approximability remains largely unexplored. A notable example of a noncommutative CSP that is not solvable in polynomial time is NC-Max-3-Cut. We present a 0.864-approximation algorithm for this problem. Our approach extends to a broader class of both classical and noncommutative CSPs. We introduce three key concepts: approximate isometry, relative distribution, and ast-anticommutation, which may be of independent interest.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- Noncommutative constraint satisfaction problems (NC-CSPs) are higher-dimensional operator extensions of classical CSPs.
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