Abstract We introduce a statistical framework for estimating Ramsey numbers by embedding two-color Ramsey instances into a ℤ 2 × 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 (α), 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 (n 2) ≈ 10 3 (n 2) 10³ 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 Erdős’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.
Fabrizio Tamburini (Mon,) studied this question.