Quantum Machine Learning

QML (IBM Qiskit)

Quantum machine learning track covering Qiskit foundations, variational methods, kernels, hardware, and advanced QML models.

Level: IntermediateDuration: 10 weeksModules: 6

Curriculum

Course Modules

Each module includes guided lectures and practical lab sessions.

Module 1

Module 1: IBM Qiskit Foundations

Lectures

  • Lecture 1.1 - Vector Spaces, Tensor Products, and Qubits
  • Lecture 1.2 - Introduction to Quantum Circuits
  • Lecture 2.1 - Simple Quantum Algorithms I
  • Lecture 2.2 - Simple Quantum Algorithms II
  • Lecture 3.1 - Noise in Quantum Computers - part 1
  • Lecture 3.2 - Noise in Quantum Computers - part 2

Labs

  • Lab 1 - Introduction to Quantum Computing Algorithms and Operations

Module 2

Module 2: Classical ML to Variational QML

Lectures

  • Lecture 4.1 - Introduction to Classical Machine Learning (ML)
  • Lecture 4.2 - Advanced Classical Machine Learning (ML)
  • Lecture 5.1 - Building a Quantum Classifier
  • Lecture 5.2 - Introduction to the Quantum Approximate Optimization Algorithm and Applications

Labs

  • Lab 2 - Introduction to Variational Algorithms

Module 3

Module 3: Quantum Kernels

Lectures

  • Lecture 6.1 - From Variational Classifiers to Linear Classifiers
  • Lecture 6.2 - Quantum Feature Spaces and Kernels
  • Lecture 7.1 - Quantum Kernels in Practice

Labs

  • Lab 3 - Introduction to Quantum Kernels and Support Vector Machines

Module 4

Module 4: Training Quantum Models

Lectures

  • Lecture 8.2 - Barren Plateaus, Trainability Issues, and How to Avoid Them
  • Lecture 8.1 - Introduction and Applications of Quantum Models

Labs

  • Lab 4 - Introduction to Training Quantum Circuits

Module 5

Module 5: Quantum Hardware and Ansatz

Lectures

  • Lecture 9.1 - Introduction to Quantum Hardware
  • Lecture 9.2 - Hardware Efficient Ansatze for Quantum Machine Learning

Labs

  • Lab 5 - Introduction to Hardware Efficient Ansatze for Quantum Machine Learning

Module 6

Module 6: Advanced Quantum Machine Learning

Lectures

  • Lecture 10.1 - Advanced QML Algorithms
  • Lecture 10.2 - The Capacity and Power of Quantum Machine Learning Models
  • The Future of Quantum Machine Learning

Labs

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