Intuitive Learning of Quantum Machine Learning

Explore How Quantum Computing Enhances AI and ML Quantum Machine Learning (QML) combines the power of quantum computing with artificial intelligence to solve complex problems more efficiently than classical methods alone.

This course provides an intuitive introduction to QML algorithms with hands-on experience using IBM Qiskit and Pennylane platforms.

Designed for participants with an interest in AI and machine learning, the course does not require advanced knowledge of quantum mechanics.

It focuses on how quantum computing can enhance classical machine learning workflows by introducing practical tools and techniques for implementing QML algorithms.

Through guided exercises and examples, the course builds strong intuition about how quantum technologies can support next-generation AI solutions.

Course Highlights Overview of artificial intelligence, machine learning, and quantum computing fundamentals Introduction to quantum machine learning algorithms Quantum gates , matrix exponentials, and rotation operations Quantum measurement and density matrix concepts Tensor products , trace, and partial trace Quantum decomposition and diagonalization Quantum Fourier Transform (QFT) and quantum arithmetic Quantum linear algorithms: PCA and SVD Quantum linear equations and HHL algorithm Quantum encoding and inner product computation Variational quantum algorithms and quantum neural networks (QNN) Quantum Support Vector Machine (QSVM) algorithms Applications of VQE, VQC, VQLS, VQSD, and VQPCA Hamiltonian simulation techniques (Trotter-Suzuki, LCU, random walk) Hands-on labs using Qiskit and Pennylane Course Benefits Build a strong foundation in classical AI, machine learning, and quantum machine learning Understand how quantum algorithms can enhance classical ML problem solving Gain practical experience implementing QML algorithms Develop intuition for quantum computing concepts without advanced physics Prepare for emerging careers in quantum AI and advanced computing Apply theoretical concepts to real-world quantum and AI use cases Credit earned may be applied toward an academic degree or professional credential, subject to the approval of the receiving institution(s) Course Details and Next Steps Course Typically Offered: Online (asynchronous) in Fall and Winter academic quarters Prerequisites: Linear algebra, calculus, basic Python skills and previously taken (CSE-31343) Intuitive Learning of Quantum Computing or any other introduction to quantum computing course.

Software: This course will utilize IBM Qiskit and Pennylane, both are free to users around the world Next step: After completing this course, consider enrolling in the courses in our Machine Learning Methods or Technical Aspects of Artificial Intelligence certificate program Contact: For more information about this course, please contact unex-techdata@ucsd.edu