A new framework, Distributed Quantum-Enhanced Optimisation (D-QEO), addresses optimisation problems in high-dimensional search spaces where classical algorithms often fail to identify global minima due to exponentially growing search volumes. Dominik Soós at Naval Research Laboratory, and colleagues in collaboration with Fermi National Accelerator Laboratory and Old Dominion University, present a system that uses quantum processing as a topographical preconditioner, not a direct solver. The approach utilises a 50-qubit space, divided into manageable sub-spaces by 5-qubit subcircuits, to generate high-quality seed points for a classical GPU-accelerated solver, avoiding limitations of near-term quantum hardware and the issues of barren plateaus. Benchmarking on established functions shows that D-QEO mitigates the exponential failure rates of classical methods and sharply reduces the computational effort needed for convergence, offering a pragmatic pathway for integrating quantum resources into complex global search tasks. Quantum topographical preconditioning overcomes exponential scaling in function optimisation The D-QEO framework achieved a striking result: it prevented the exponential failure rates seen in purely classical algorithms when optimising high-dimensional functions, a feat previously unattainable with standard methods. Observed on benchmark functions, specifically the 10-dimensional Rastrigin and Ackley functions, classical algorithms typically falter due to the exponentially growing search space. As the number of variables increases, the volume of the search space expands exponentially, rendering exhaustive search impractical and stochastic methods increasingly unreliable. This phenomenon, known as the ‘curse of dimensionality’, severely limits the applicability of classical optimisation techniques to complex, real-world problems. Utilising a 50-qubit quantum processing unit (QPU) as a topographical preconditioner, the system effectively narrows the search area and generates high-quality seed points for a classical GPU-accelerated solver to refine, circumventing limitations of near-term quantum hardware. The quantum component doesn’t attempt to solve the optimisation problem directly, but rather to intelligently reshape the search landscape, making it more amenable to classical algorithms.