Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Quantum Machine Learning: Harnessing Quantum Computing for ML sits at the frontier of two transformative fields. You build the necessary quantum-computing intuition, then explore how it meets machine learning: quantum algorithms with potential speedups, quantum feature maps and kernels, variational quantum circuits, and where quantum might genuinely help learning. The course is honest about today’s hardware limits versus the long-term promise. You finish able to reason critically about quantum machine learning and its prospects. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers quantum machine learning — how quantum computing and machine learning combine, from quantum algorithms for ML to quantum-enhanced models.
📋 Course Objectives
1. Explain the essentials of quantum computing.
2. Understand quantum feature maps and kernels.
3. Explore variational quantum circuits.
4. Compare quantum with classical ML.
5. Judge realistic near-term prospects.
👥 Who Should Enroll?
• ML researchers and quantum enthusiasts
• Data scientists exploring quantum
• Physics and CS researchers
• Students of quantum computing
🚀 Key Learning Outcomes
• A critical understanding of quantum ML.
• A quantum-plus-ML perspective.
• A foundation at the quantum-AI frontier.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Quantum Machine Learning Foundations
Analyze the mathematical prerequisites for quantum machine learning, including linear algebra, differential equations, and probability theory • Develop a comprehensive understanding of quantum computing concepts, such as superposition, entanglement, and quantum measurement • Evaluate the applications of quantum machine learning in various domains, including computer vision, natural language processing, and recommender systems
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines for quantum machine learning using tools such as Apache Beam and TensorFlow • Configure data preprocessing techniques, including data normalization, feature scaling, and dimensionality reduction • Optimize data storage and retrieval systems for quantum machine learning applications using databases such as MongoDB and Cassandra
Model Architecture, Algorithm Design, and Quantum Machine Learning Methods
Implement quantum machine learning algorithms, including quantum k-means, quantum support vector machines, and quantum neural networks • Develop and evaluate different model architectures for quantum machine learning, including convolutional neural networks and recurrent neural networks • Analyze the computational complexity and scalability of quantum machine learning algorithms using metrics such as time and space complexity
Training, Hyperparameter Optimization, and Evaluation
Configure and train quantum machine learning models using optimization algorithms such as gradient descent and Adam • Evaluate the performance of quantum machine learning models using metrics such as accuracy, precision, and recall • Develop and implement hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization
Deployment, MLOps, and Production Workflows
Design and implement deployment pipelines for quantum machine learning models using tools such as Docker and Kubernetes • Configure and manage production workflows for quantum machine learning applications using tools such as Apache Airflow and Zapier • Develop and evaluate monitoring and logging systems for quantum machine learning applications using tools such as Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of quantum machine learning applications, including bias, fairness, and transparency • Develop and implement bias mitigation techniques, including data preprocessing, feature engineering, and model regularization • Evaluate the responsible AI practices for quantum machine learning applications, including explainability, accountability, and human oversight
Industry Integration, Business Applications, and Case Studies
Develop and evaluate business cases for quantum machine learning applications, including cost-benefit analysis and return on investment • Analyze the industry trends and applications of quantum machine learning, including finance, healthcare, and transportation • Implement and evaluate quantum machine learning solutions for real-world business problems using case studies and simulations
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Qiskit |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
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