Sparse Walsh-Pauli Correlation Encoding for MaxCut: A Quantum-Inspired Formulation with Quantum-Hardware Validation
We introduce a sparse Walsh-Pauli correlation encoding for MaxCut, in which graph variables are represented by diagonal Pauli-string expectation values computed classically as normalized XOR autocorrelations of Walsh coefficients. The complete Walsh basis can reproduce the diagonal correlators of arbitrary quantum states, while a structured sparse support reduces the number of trainable coefficients to $m+1$ for $m$ graph variables. We evaluate the method on $1{,}400$ held-out random graphs with up to $7{,}000$ vertices, obtaining empirical approximation ratios near $0.98$ relative to MQLib heuristic references. On Gset instances G14, G23, G60, and G64, the sparse model achieves mean refined approximation ratios of $0.9828$, $0.9926$, $0.9781$, and $0.9369$, respectively, with $80.4\%$-$89.3\%$ fewer parameters than the complete Walsh representation. The trained coefficients are transferred without retraining to quantum circuits. Statevector simulations of the four Gset instances achieve fidelities above $0.99994$ and preserve all correlator signs. Separately, a seven-qubit experiment on a $15$-vertex graph executed on IBM quantum hardware achieves a correlator-sign agreement of $0.927\pm0.049$ relative to the corresponding noiseless circuit. These results demonstrate the potential of sparse Walsh-PCE for large-instance quantum-inspired MaxCut optimization while validating the transfer of classically trained parameters to quantum hardware.