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Quantum Optimization
Simulating Gaussian boson sampling on graphs in polynomial time
arXiv
Authors: Konrad Anand, Zongchen Chen, Mary Cryan, Graham Freifeld, Leslie Ann Goldberg, Heng Guo, Xinyuan Zhang
Year
2025
Paper ID
16864
Status
Preprint
Abstract Read
~2 min
Abstract Words
58
Citations
N/A
Abstract
We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time. Graphical applications of GBS typically sample from this distribution, and thus quantum algorithms do not provide exponential speedup for these applications. We also show that another distribution related to Boson sampling can be sampled classically in polynomial time.
Why This Paper Matters
- This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time.
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