Having established the theoretical foundations and algorithmic structures of advanced QML, we now turn to the practical considerations of executing these algorithms on physical quantum hardware. Current quantum processors operate in the Near-Term Intermediate-Scale Quantum (NISQ) era, characterized by limited qubit counts, connectivity, and significant susceptibility to noise. Factors like decoherence, gate imperfections, and readout errors introduce discrepancies between ideal computations and actual results. These imperfections directly influence the training process and final performance of QML models, potentially obscuring any quantum advantage.

This chapter focuses on addressing these hardware realities. You will learn to:

• Characterize different types of quantum hardware noise.
• Analyze the effects of noise on QML algorithm execution.
• Apply quantum error mitigation techniques, such as Zero-Noise Extrapolation (ZNE) and Probabilistic Error Cancellation (PEC).
• Optimize and transpile quantum circuits to match hardware constraints.
• Design hardware-efficient circuit ansätze.
• Understand methods for benchmarking QML algorithms on quantum devices.

Gaining proficiency in these areas is necessary for developing QML applications that can function effectively on present-day and future quantum computers.