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Quantum Machine Learning Course with Python [2025]
Rating: 3.4 out of 5(15 ratings)
140 students

Quantum Machine Learning Course with Python [2025]

Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Support Vector Machines (QSVMs)
Created byHoang Quy La
Last updated 9/2025
English

What you'll learn

  • Pennylane
  • Introduction to quantum feature map
  • Introduction to Quantum Data Encoding
  • Quantum Kernel Method
  • Introduction to Quantum Clustering algorithm
  • Implementation of Quantum Fuzzy Clustering
  • Introduction to Quantum Support Vector Machine
  • Introduction to Variational Quantum Classifier (VQC)
  • What is Quantum K-Means Clustering
  • 3D Optimized Quantum k-Means
  • quantum deep learning
  • Quantum Neural Networks (QNN)
  • Quantum Convolutional Neural Networks
  • QGAN
  • QGAN implementation with 3-D data
  • Quantum Transfer Learning

Course content

4 sections • 60 lectures • 8h 10m total length

  • Course Structure1:09
  • How to make the most of this course1:52
  • Tools used in this course1:35

    Highlight Google Colab as the main tool for easy setup and library installation, with an option to download the course as a Jupyter notebook for offline data access.

  • Introduction to Quantum machine learning (QML)3:23
  • Why do we need to use Quantum Machine Learning5:29

    Explore why quantum machine learning combines quantum computing with classical machine learning to speed up computation and enable higher dimensional features for drug discovery and financial modeling.

  • Basic concepts of machine learning7:13
  • Introduction to pandas3:08
  • Introduction to numpy6:17
  • Introduction to matplotlib4:11
  • Basics of supervised, unsupervised, and reinforcement6:02
  • Neural network and optimization in machine learning6:56
  • Classical Kernel Methods and Support Vector Machines5:29
  • Simple SVM implementation14:54
  • Introduction to PennyLane3:16
  • Simple implementation of PennyLane4:55
  • Introduction to PyTorch2:10

Requirements

  • Basic and advanced python knowledge is required
  • Basic quantum computing is required

Description

Quantum Machine Learning with Python [2025]

Are you ready to step into the future of Artificial Intelligence and Quantum Computing?
This course is designed to introduce you to the rapidly growing field of Quantum Machine Learning (QML) — a fusion of quantum computing principles with powerful machine learning techniques. By the end of this course, you will be equipped with the knowledge and skills to build and experiment with quantum-enhanced models using Python and cutting-edge frameworks.

What You’ll Learn

  • Foundations of Quantum Computing: qubits, superposition, entanglement, and quantum gates.

  • Core Machine Learning workflows and how they extend to quantum systems.

  • Implement Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Support Vector Machines (QSVMs).

  • Explore Quantum Generative Adversarial Networks (QGANs) for data generation.

  • Apply Quantum Transfer Learning with pre-trained classical models.

  • Hands-on coding with PennyLane, Qiskit, and PyTorch.

  • Real-world projects: MNIST classification, CIFAR-10 transfer learning, quantum clustering, and more.

Why Take This Course?

Quantum computing is no longer science fiction — it’s shaping industries from finance and healthcare to AI and cybersecurity. Learning QML today places you at the forefront of this technological revolution. With Python as your guide, you’ll bridge the gap between theory and practice through hands-on labs, coding projects, and step-by-step implementations.

Who Is This Course For?

  • Students and developers eager to enter the quantum AI field.

  • Machine learning practitioners curious about quantum algorithms.

  • Researchers and innovators preparing for the next wave of computing.

Join the course today and become part of the quantum-ready workforce of 2025.
Enroll today and unlock the future of Quantum Machine Learning with Python!

Who this course is for:

  • Anyone who wants to learn about quantum machine learning
  • Anyone who wants to improve python
  • Anyone who wants to become quantum machine learning engineers

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