Quantum Naive Bayes: Calculating Posterior Probability

As quantum computing continues to develop, understanding how to implement classical machine learning algorithms on quantum hardware is becoming a vital skill. This course guides you through the fundamental mathematics and circuit designs required to calculate posterior probabilities within a quantum framework. By reading this course, you will transition from classical probability concepts to quantum state representations, gaining a clear, step-by-step understanding of how to map probability distributions onto qubits and execute the controlled rotations necessary for quantum Naive Bayes classification. What you'll learn: - Understand the foundational concepts of classical Bayes' theorem and posterior probability. - Map classical probability distributions to quantum states using amplitude encoding. - Apply controlled-rotation gates to represent conditional probabilities on quantum circuits. - Calculate posterior probabilities by analyzing quantum measurement outcomes. - Explore modern hybrid classical-quantum workflows for classification tasks. - Practice writing clean, readable quantum simulator code to verify your probability calculations. This text-based course begins with essential probability theory and basic quantum computing concepts before moving into hands-on circuit design. You will work through structured written explanations, mathematical derivations, and step-by-step code walkthroughs that demonstrate how to construct and measure your quantum classifier. This course is designed for beginners in quantum machine learning, and no prior experience with quantum hardware is required, though a basic understanding of linear algebra and Python is helpful. Start reading today to build your foundational skills in quantum classification.