Before claiming a Quantum advantage, what can classical computers already solve?

When evaluating quantum algorithms for combinatorial optimization, the comparison is only meaningful if the classical baseline is taken seriously. I created a technical walkthrough examining how Gurobi, a state-of-the-art classical optimization solver, handles QUBO problems. The purpose is to establish a practical classical reference before moving on to quantum annealers and variational quantum algorithms. The video begins with weighted Max-Cut, derives its QUBO representation, and implements the resulting quadratic binary model in Python using gurobipy. It then explores: - exact versus heuristic approaches to QUBO; - Gurobi’s branch-and-bound search and bound convergence; - primal heuristics for finding high-quality incumbents; - why finding a solution and proving optimality are different tasks; - how MIPGap trades optimality guarantees for runtime; - why runtime depends heavily on the specific problem instance; - the effect of dense versus sparse QUBO matrices; - deterministic behavior under fixed parameters and hardware; - and practical access through Gurobi’s academic licensing. The larger point is not that classical solvers make quantum optimization unnecessary. It is that claims of quantum utility require carefully designed comparisons against highly optimized classical methods. A useful benchmark should consider more than wall-clock runtime: - solution quality and optimality gap; - instance distribution and graph density; - preprocessing and model-conversion costs; - time to the first good solution; - total time required to certify optimality; - solver parameter tuning; - hardware and reproducibility; - and end-to-end execution overhead. Video: https://youtu.be/TB1ny8o4ImQ I’d be interested in the community’s view: which classical baselines and metrics should be considered essential when benchmarking quantum annealing or variational algorithms on QUBO problems? submitted by /u/Future_Ad7567 [link] [comments]