Course
Introduction to Quantum Machine Learning
CSE-41406
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 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
Who Should Take This Course?
- AI and machine learning practitioners exploring quantum technologies
- Data scientists and software engineers interested in next-generation computing
- University students in computer science, engineering, or related fields
- Researchers working in artificial intelligence or quantum computing
- Technology professionals seeking skills in quantum machine learning
- Innovators and entrepreneurs interested in quantum-enhanced AI solutions
Course Information
Online
4.00 units
$795.00
Course sessions
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Instructor:
James C.S. Meng, Ph.D., MSM is a currently a senior fellow at UC San Diego – Super Computer Center and a visiting scholar at Massachusetts Institute of Technology - Sloan School of Management. Dr. Meng held various positions with the federal government’s senior executive service from June 1998 until June 2015 when he retired as deputy assistance secretary of the Navy, Business Enterprise Solutions. Dr. Meng has a B.S. in mechanical engineering from Taiwan University, an M.S. in engineering physics from UC Berkley, an M.S. in management from the Massachusetts Institute of Technology - Sloan School of Management and a Ph.D. in aeronautical engineering from UC Berkley.
Dr. Meng is an established author with numerous articles published in various journals including the Journal of Fluid Mechanic, Journal of Applied Optics and Journal of Computational Physics. He has published technical reports on laser doppler velocimetry, superconducting electromagnetic thruster (SCEMT ), electric propulsion technology, experimental studies of turbulence reduction, simulations of hydrodynamics wake in oceanic environment, theoretical analysis of internet waves, and hypersonic reentry vehicle dynamics. In addition, Dr. Meng has obtained six patents in SCEMT, elecomagnetohydrodynamic boundary layer control and acoustic remote cavitation.
Full Bio
Section ID:
196337
Class type:
Online Asynchronous.
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
No textbook required.
Policies:
- No refunds after: 4/6/2026
Schedule:
No information available at this time.
Instructor:
James Meng
James Meng
Dr. Meng is an established author with numerous articles published in various journals including the Journal of Fluid Mechanic, Journal of Applied Optics and Journal of Computational Physics. He has published technical reports on laser doppler velocimetry, superconducting electromagnetic thruster (SCEMT ), electric propulsion technology, experimental studies of turbulence reduction, simulations of hydrodynamics wake in oceanic environment, theoretical analysis of internet waves, and hypersonic reentry vehicle dynamics. In addition, Dr. Meng has obtained six patents in SCEMT, elecomagnetohydrodynamic boundary layer control and acoustic remote cavitation.