Building Hybrid Quantum–Classical Computing Ecosystems: A Survey

Hybrid quantum–classical computing represents a pragmatic path toward harnessing the capabilities of quantum devices. Instead of treating quantum processors as standalone systems, they are integrated with powerful classical resources to form a complementary workflow. In this paradigm, each component of the computing stack plays a specific role: classical systems handle tasks that are scalable, but often complicated and data-intensive, while quantum hardware contributes non-classical primitives such as superposition and entanglement that are difficult to emulate efficiently. This review surveys the emerging quantum–classical ecosystem, emphasizing the workflows, system components, and application areas that define this paradigm. We structure the discussion around three key stages: pre-processing, which prepares quantum circuits before execution to reduce noise and minimize resource usage; co-processing, where dynamically adaptive methods leverage both application and device characteristics to enhance hybrid algorithms and enable scalable quantum error correction; and post-processing, which reconstructs high-fidelity outputs from noisy measurements to ensure reliable results.