Matrix-Free GPU Semidefinite Programming for Quantum Ordered Search at the k=6 Frontier

Quantum computation offers the potential for a significant constant-factor speedup for the Ordered Search Problem (OSP). A classical construction is the $k$-query quantum ordered search algorithm, which can exactly search an $N$-element ordered list and achieves a query complexity improvement of a factor of $\frac{k}{\log_2 N}$. For larger $k$, stronger constant-factor improvements could be obtained by finding the largest admissible list size $N^\star$, a task that can be formulated as a structured semidefinite program (SDP). However, solving this SDP becomes computationally intractable beyond $k=6$, as existing CPU and GPU solvers rely on explicit construction of prohibitively large constraint matrices. In this paper, we introduce a matrix-free GPU SDP framework that evaluates the highly structured constraints in OSP on-the-fly using custom CUDA kernels, reducing memory complexity from quadratic to linear and shifting the bottleneck from memory to computation. Using this approach, we tightly bracket the optimal list size for $k=6$ as $90,000 \le N^\star < 94,000$, improving the best known upper bound on the query coefficient from $0.390$ to $0.365$. We further certify these results by constructing rigorous dual infeasibility certificates via matrix-free minimum-eigenvalue estimation.