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Abstract

We introduce a statistical framework for estimating Ramsey numbers by embedding two-color Ramsey instances into a $Z_2 \times Z_2$-graded Majorana algebra. This approach replaces brute-force enumeration with two randomized spectral diagnostics applied to operators of a given dimension d associated with Ramsey numbers: a linear projector $P_{lin}$ and an exponential map $P_{exp}(\alpha)$, suitable for both classical and quantum computation. In the diagonal case, both diagnostics identify R(5,5) at n=45. The quantum realizations act on a reduced module and therefore require only five data qubits plus a few ancillas via block-encoding/qubitization for R(5,5)=45, in stark contrast to the $\binom{n}{2} \approx 10^3$ logical qubits demanded by direct edge encodings. We also provide few-qubit estimates for R(6,6) and R(7,7), and propose a simple "prime-sequence" consistency heuristic that connects R(5,5)=45 to constrained diagonal growth. Our method echoes Erd\H{o}s's probabilistic paradigm, emphasizing randomized arguments rather than explicit colorings, and parallels the classical coin-flip approach to Ramsey bounds. Finally, we discuss potential applications of this framework to machine learning with a limited number of qubits.