Hybrid Quantum–Classical ML Systems for Complex Problem Solving

Abstract

Hybrid quantum–classical machine learning (HQC-ML) systems combine quantum computing primitives with classical machine-learning pipelines to address computationally hard problems. This paper reviews the current landscape of HQC-ML, surveys relevant literature, analyzes existing systems, and proposes a concrete hybrid architecture optimized for combinatorial optimization and high-dimensional data modeling. We present an experimental design and expected results comparing purely classical baselines against the proposed hybrid approach on representative problem classes (vehicle routing and molecular property prediction). The paper concludes with a discussion of limitations, implementation considerations, and future research directions.