Quantum-Classical Hybrid AI

Quantum computers excel at certain combinatorial optimization problems, max-cut, traveling salesman, portfolio optimization, that are intractable for classical algorithms at scale. But quantum hardware is noisy, expensive, and not yet widely deployed. Can we build hybrid systems that combine classical neural networks with quantum subroutines where they add value?

We designed a framework where a classical model predicts which subproblems benefit most from quantum solving, and a small quantum coprocessor handles only those subproblems. The rest is solved classically. This reduces quantum runtime by 80% in our benchmarks while maintaining solution quality.

We implemented the pipeline on IBM Quantum and AWS Braket, comparing against purely classical baselines (simulated annealing, genetic algorithms) and purely quantum approaches. The hybrid consistently outperformed both on problems with 50–200 variables.

Our findings suggest that near-term quantum advantage is most likely in hybrid settings, not replacement of classical ML, but augmentation for specific subroutines. We release the code and benchmarks for reproducibility.