15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of data ethics and responsible AI.
- Master bias detection and mitigation techniques in data and models.
- Utilize fairness metrics to evaluate AI system performance.
- Implement transparency and explainability in AI applications.
- Design and build ethical data science workflows.
- Optimize AI systems for compliance with ethical guidelines and regulations.
- Troubleshoot and address common ethical challenges in data science.
- Implement privacy-preserving techniques in data analysis.
- Integrate ethical considerations into real-world AI projects.
- Understand how to handle sensitive data and ensure data governance.
- Explore advanced ethical frameworks and regulatory landscapes.
- Apply real world use cases for data ethics and responsible AI.
- Leverage ethical AI tools and frameworks for efficient development.
About the Course
This advanced training explores the intersection of quantum computing, machine learning, and strategic innovation, providing participants with a practical understanding of how emerging quantum technologies may transform data analytics, optimization, artificial intelligence, and complex decision-making.
The course introduces quantum computing fundamentals alongside quantum machine learning concepts and hybrid quantum-classical approaches. Participants will examine potential applications in business optimization, financial modelling, supply chain management, cybersecurity, research, and strategic planning while learning how to evaluate the maturity, feasibility, and potential business value of quantum technologies.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of quantum machine learning.
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Workshop 1.2:
Overview of quantum computing concepts and their application to ML.
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Workshop 1.3:
Setting up a quantum machine learning development environment.
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Workshop 1.4:
Introduction to quantum machine learning libraries and platforms.
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Workshop 1.5:
Best practices for quantum machine learning.
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Workshop 2.1:
Implementing quantum algorithms for machine learning tasks.
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Workshop 2.2:
Utilizing quantum support vector machines (QSVMs).
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Workshop 2.3:
Designing and building quantum algorithms for data classification.
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Workshop 2.4:
Optimizing quantum algorithms for specific machine learning problems.
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Workshop 2.5:
Best practices for quantum algorithms.
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Workshop 3.1:
Implementing quantum neural networks for data analysis and model building.
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Workshop 3.2:
Utilizing quantum circuits as neural network architectures.
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Workshop 3.3:
Designing and building quantum neural network models.
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Workshop 3.4:
Optimizing quantum neural networks for data representation.
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Workshop 3.5:
Best practices for quantum neural networks.
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Workshop 4.1:
Implementing hybrid quantum-classical models for enhanced performance.
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Workshop 4.2:
Utilizing variational quantum algorithms (VQAs).
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Workshop 4.3:
Designing and building hybrid models for data analysis.
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Workshop 4.4:
Optimizing hybrid models for quantum resource efficiency.
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Workshop 4.5:
Best practices for hybrid models.
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Workshop 5.1:
Designing and building quantum machine learning experiments.
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Workshop 5.2:
Utilizing quantum simulators and quantum hardware.
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Workshop 5.3:
Implementing experimental design and data collection.
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Workshop 5.4:
Optimizing experiments for reproducibility and accuracy.
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Workshop 5.5:
Best practices for experiments.
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Workshop 6.1:
Optimizing quantum machine learning algorithms for specific applications.
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Workshop 6.2:
Utilizing quantum circuit optimization techniques.
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Workshop 6.3:
Implementing quantum error mitigation strategies.
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Workshop 6.4:
Designing scalable quantum machine learning solutions.
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Workshop 6.5:
Best practices for algorithm optimization.
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Workshop 7.1:
Debugging common challenges in quantum machine learning.
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Workshop 7.2:
Analyzing quantum circuit performance and errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common quantum machine learning issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing quantum feature maps and kernel methods.
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Workshop 8.2:
Utilizing quantum kernels for data classification.
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Workshop 8.3:
Designing and building quantum feature extraction techniques.
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Workshop 8.4:
Optimizing feature maps for quantum advantage.
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Workshop 8.5:
Best practices for feature maps.
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Workshop 9.1:
Integrating quantum machine learning with classical data processing.
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Workshop 9.2:
Utilizing hybrid data pipelines and workflows.
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Workshop 9.3:
Implementing classical pre- and post-processing steps.
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Workshop 9.4:
Optimizing integration for data transfer and computation.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle noise and errors in quantum computations.
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Workshop 10.2:
Utilizing quantum error correction and mitigation techniques.
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Workshop 10.3:
Designing and building noise-resilient quantum algorithms.
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Workshop 10.4:
Optimizing quantum circuits for error reduction.
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Workshop 10.5:
Best practices for noise handling.
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Workshop 11.1:
Exploring advanced quantum machine learning techniques (variational quantum algorithms, quantum generative models).
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Workshop 11.2:
Exploring advanced quantum machine learning techniques (variational quantum algorithms, quantum generative models).
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Workshop 11.3:
Implementing advanced variational quantum algorithms.
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Workshop 11.4:
Designing and building advanced quantum machine learning solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing quantum machine learning for drug discovery and materials science.
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Workshop 12.2:
Utilizing quantum machine learning for financial risk modeling.
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Workshop 12.3:
Implementing quantum machine learning for optimization problems.
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Workshop 12.4:
Utilizing quantum machine learning for natural language processing.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing Qiskit, Cirq, and PennyLane for quantum machine learning.
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Workshop 13.2:
Implementing quantum machine learning models with libraries.
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Workshop 13.3:
Designing and building quantum machine learning pipelines.
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Workshop 13.4:
Optimizing library usage for efficient development.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing model evaluation and performance analysis for quantum machine learning.
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Workshop 14.2:
Utilizing metrics for quantum circuit performance and model accuracy.
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Workshop 14.3:
Designing and building benchmarking frameworks.
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Workshop 14.4:
Optimizing evaluation for quantum advantage.
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Workshop 14.5:
Best practices for evaluation.
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Workshop 15.1:
Emerging trends in quantum machine learning.
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Workshop 15.2:
Utilizing fault-tolerant quantum computers for machine learning.
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Workshop 15.3:
Implementing quantum machine learning in cloud-based quantum services.
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Workshop 15.4:
Best practices for future applications.
