Quantum Query Algorithms for the Constructive Diagonal Ramsey Theorem

📅 2026-09-05
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📝 Abstract
The constructive diagonal Ramsey problem asks, given adjacency-oracle access to an $N$-vertex graph, for a clique or independent set of the order guaranteed by Ramsey's theorem. We give a bounded-error quantum algorithm that, for every $K\ge2$ and $N\ge4^{K-1}$, finds and verifies a homogeneous $K$-set using $O\!\left(2^K K\log\frac K\eta\right)$ edge queries with failure probability at most $\eta$. At the Ramsey scale $N=2^n$, this yields a homogeneous set of order $\lfloor n/2\rfloor+1$ using $O(\sqrt N\log N\log(\log N/\eta))$ queries, improving on the $O(N)$ queries of the explicit classical recursion and giving, to our knowledge, the first sublinear worst-case algorithm for the Ramsey relation. We also derive an $\Omega(N^{1/12})$ quantum lower bound by a reduction from collision finding. The algorithm runs the constructive recursion over implicit candidate sets. Each set is represented by a short conjunction of adjacency constraints and sampled using capped unknown-solution quantum search, and a scale-aware concentration schedule balances estimation accuracy against the increasing cost of sampling deeper sets. We complement the upper bound with an $\Omega(N^{1-1/\sqrt2})$ randomized lower bound, transported from the random-Painter analysis of online Ramsey numbers, which holds on the uniform distribution $G(N,1/2)$. On that distribution a greedy quantum search uses only $\widetilde O(N^{1/4})$ queries, giving a provable polynomial quantum speedup for Ramsey search on random graphs. We also give an estimation-free size-biased recursion and extend it to every fixed number of edge colours.
Problem

Research questions and friction points this paper is trying to address.

Quantum Algorithm
Constructive Diagonal Ramsey Theorem
Edge Queries
Innovation

Methods, ideas, or system contributions that make the work stand out.

quantum algorithm
Ramsey theorem
sublinear query complexity
bounded-error
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C
Cheng Xin
Department of Computer Science, California State University, Fresno