Quantum Machine Learning in Practice A hands-on guide for ML Engineers Exploring Hybrid Quantum-Classical Models ／Jeremy Samuelson （Packt Publishing）

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Build and evaluate real quantum machine learning models using Python frameworks, hybrid workflows, and disciplined benchmarking to separate practical insight from hypeKey FeaturesDesign hybrid quantum–classical ML models using Python frameworksBenchmark QML against classical baselines with rigorous evaluationBuild an end-to-end simulator-based quantum ML workflowBook DescriptionQuantum computing is advancing rapidly, yet practical guidance for machine learning engineers remains limited. Most resources emphasize physics or theory, leaving practitioners unsure how quantum methods fit into real-world ML workflows. 'Quantum Machine Learning in Practice' addresses this gap with a hands-on, Python-first approach built for data scientists and ML engineers. Rather than presenting quantum models as replacements for classical ML, this book focuses on disciplined experimentation, hybrid architectures, and rigorous benchmarking. You will learn how classical data is encoded into quantum circuits, h

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