Quantum-Classical Hybrid Computing is a computational paradigm combining classical processors executing conventional algorithms with quantum processors exploiting quantum mechanical phenomena — Quantum computing leverages superposition and entanglement enabling exponential speedups for specific problems, but requires classical systems for initialization, measurement, and control. Quantum Processor Characteristics implement qubits maintaining superposition, entanglement enabling correlations, and unitary operations implementing quantum gates, requiring extreme isolation from environmental noise. Problem Decomposition identifies quantum-suitable subroutines where quantum speedups apply, leverages classical processing for portions where quantum offers no advantage. Variational Algorithms employ hybrid approaches where quantum processors evaluate ansatze, classical processors optimize parameters, iterating until convergence. Error Mitigation exploits classical post-processing correcting quantum measurement errors, implements readout error correction mitigating measurement uncertainties. Measurement Processing performs classical analysis on quantum measurement results, extracts problem solutions from measurement statistics. Barren Plateaus avoid optimization landscapes with vanishing gradients through classical optimization strategies, classical preprocessing improving initialization. Scaling envisions future hybrid systems with thousands of qubits coupled to powerful classical systems, enabling previously intractable computations. Quantum-Classical Hybrid Computing represents the practical approach to near-term quantum computing.