Foundations of Quantum Bayesian Networks in Machine Learning

Traditional probabilistic classifiers often struggle with complex, overlapping dependencies in real-world data. Quantum Bayesian Networks offer a powerful alternative by leveraging quantum superposition and interference to model intricate probabilistic relationships. This text-based course guides you through the core principles of quantum probability, showing you how to conceptualize and design quantum classifiers that overcome classical limitations. What you'll learn: - Understand the fundamental shift from classical probability to quantum probability amplitudes. - Analyze the limitations of classical Naïve Bayes classifiers and how quantum models resolve them. - Explore the role of quantum oracles in representing conditional probability distributions. - Design quantum Bayesian network structures using modern quantum programming concepts. - Apply quantum interference principles to update beliefs and perform classification tasks. - Evaluate hybrid quantum-classical workflows for modern machine learning applications. You will start with foundational definitions of quantum states and probability before progressing to structured network design and step-by-step classification workflows. The material is presented through clear written explanations, structured walkthroughs, and practical code snippets using open-source quantum SDKs. This course is designed for beginners in quantum machine learning, requiring no prior background in quantum physics. Start exploring the future of probabilistic machine learning today.