Multi-Paradigm Architectural Taxonomy of Hybrid Quantum-Classical Learning Pipelines: From Sequential Offloading to Cloud-Native MLOps Orchestration

Abstract

The combination of quantum computing and machine learning has led to the development of a new breed of hybrid quantum-classical learning pipelines that use the best features of both classical and quantum processors. Although quantum hardware is still limited by noise, small number of qubits, and short coherence times, hybrid architectures represent a realistic way towards quantum advantage in the near-term by delegating the more difficult computational parts to Quantum Processing Units (QPUs) while keeping the classical infrastructure for data preparation, optimization, and orchestration. This work surveys thoroughly design patterns and resource optimization strategies for hybrid quantum-classical learning pipelines running on clouds. Initially, a characterization of design patterns including sequential offloading, incremental replacement, parallel ensemble, and cloud-native MLOps is derived, and their appropriateness to different application domains as well as quantum resource constraints are discussed. Next, we explore resource allocation techniques ranging from static circuit partitioning to dynamic qubit-sharing, highlighting hardware-aware placement, fidelity-aware selection, and Kubernetes-based container orchestration. Then we talk about scheduling and optimization frameworks, classical schedulers converted to quantum workloads, AI-driven orchestration with deep reinforcement learning and graph neural networks, variational loop optimizations, and quantum-assisted classical scheduling. Besides the platforms, we review also open source tools and simulators. Implementation landscapes across major cloud platforms (IBM Quantum, AWS Braket) and open-source orchestrators (Qiboml Qubernetes CONQURE, HALO) are surveyed, alongside simulation environments.