!
You're viewing papers too quickly. Please wait a moment.<br>This helps keep the archive available for everyone.
Quick Navigation
Topics
Quantum Algorithms
Quantum gambling based on Nash-equilibrium
arXiv
Authors: Pei Zhang, Xiao-Qi Zhou, Yun-Long Wang, Peter J. Shadbolt, Yong-Sheng Zhang, Hong Gao, Fu-Li Li, Jeremy L. O'Brien
Year
2014
Paper ID
47871
Status
Preprint
Abstract Read
~2 min
Abstract Words
110
Citations
N/A
Abstract
A fair gambling is hard to be made between two spatially separated parties without introducing a trusted third party. Here we propose a novel gambling protocol, which enables fair gambling between two distant parties without the help of a third party. By incorporating the key concepts and methods of game theory, our protocol will force the two parties to move their strategies to a Nash-equilibrium point which guarantees the fairness through the physical laws of quantum mechanics. Furthermore, we show that our protocol can be easily adapted to a biased version, which would find applications in lottery, casino, etc. A proof-of-principle optical demonstration of this protocol is reported as well.
Why This Paper Matters
- It adds a 2014 reference point for readers tracking recent quantum research.
- A fair gambling is hard to be made between two spatially separated parties without introducing a trusted third party.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.