Quantum Machine Learning (QML)
The Quantum Machine Learning course explores the integration of machine learning and quantum computing. Focusing on quantum-classical hybrid models, the course examines methods that utilize unique quantum properties such as superposition, entanglement, and interference to develop advanced machine learning algorithms. The course is designed to provide an understanding of the theoretical foundations and practical implementation of QML algorithms on next-generation NISQ (Noisy Intermediate-Scale Quantum) computers.
- Category
- Quantum Computing
- Status
- Upcoming
Course overview
Quantum Machine Learning (QML), as one of the most important pathways for the practical utilization of quantum computers, is expected to play a significant role in various industries in the coming years:
Artificial Intelligence and Big Data: Improving model training speed and processing large-scale datasets in areas such as market analysis, image recognition, and natural language processing.
Pharmaceuticals and Computational Chemistry: Optimizing drug design and molecular structure modeling using quantum machine learning models.
Cybersecurity and Cryptography: Applications in pattern analysis and anomaly detection in emerging cryptographic systems.
Finance and Market Prediction: Faster and more accurate analysis of financial data using quantum neural networks and quantum reinforcement learning algorithms.
Global trends, including investments by companies such as IBM, Google, Amazon, Xanadu, and Wave-D, indicate that expertise in QML will soon become one of the key skills in the field.
Course details
- Start date
- 11/22/2026
- End date
- 12/29/2026
- Registration deadline
- 11/21/2026
Who is this course for?
- Individuals interested in the future of quantum computing and quantum algorithms.
- Students in physics, computer engineering, and electrical engineering.
Course objectives
- Understanding the fundamental principles of quantum computing and its connection with machine learning.
Course syllabus
- 1
Review of Machine Learning Principles
Review of machine learning concepts and classical deep learning. Brief history and motivation for their implementation. - 2
Fundamentals of Quantum Computing
Definition of qubits, superposition, and entanglement. Basic quantum gates and quantum circuits. Setting up and installing modules and packages related to quantum architectures, including Qiskit-Machine-Learning and Qiskit. Familiarization with the structure of well-known quantum algorithms and their implementation in Qiskit. Understanding quantum advantage in certain applications. - 3
Quantum Circuit Programming
Introduction to Qiskit V2.x. Implementation of prominent algorithms in this field.
Course instructors
- S
Sina Asadian
Instructor
Enroll in this course
This course runs in hybrid format
Pick how you attend — the price, schedule and enrollment on this page follow your choice.
- Start date
- 11/22/2026
- Register by
- 11/21/2026
- Sessions
Frequently asked questions
Course Prerequisites
Required Prerequisites: Familiarity with Artificial Neural Networks (ANN) and the concept of learning. Familiarity with organizing and managing conventional deep learning datasets and neural networks. Familiarity with quantum concepts, quantum circuits, quantum hardware operation, quantum measurement, and related topics. Familiarity with Python; all codes will be written in Python. Preferred Prerequisites: Familiarity with existing optimizers in the field of machine learning and neural networks. Familiarity with one of the deep learning frameworks such as PyTorch, TensorFlow, or Keras (preferably). Familiarity with gradient-based algorithms such as Gradient Descent and convex optimization.
Expected Learning Outcomes
By the end of this course, participants are expected to be able to: Explain the fundamental concepts of QML, including different types of data encoding in quantum architectures and model training methods. Design Parametric Quantum Circuits (PQC) and train them using real-world data. Implement a complete QML project, including data encoding, model design, optimizer selection, training, and evaluation. Develop the ability to research and evaluate recent studies in the field of QML and apply them in their work.
