Quantum Computing Capstone CAP-QC-QSVM.v1

Quantum Support Vector Machine (QSVM) Exploration

This capstone explores QSVM for quantum-enhanced classification. Participants will implement QSVM, compare classical vs. quantum SVM, and analyze performance on real-world datasets using quantum computing frameworks.

This Course Includes:

5 hours on-demand video | 7 Articles | 8 Downloadable Resources
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Certificate of completion
  • Understand quantum kernels in machine learning.
  • Implement QSVM using Qiskit.
  • Compare classical vs. quantum SVM performance.
  • Train models on real-world datasets.
  • Optimize hyperparameters for quantum classification.
  • Evaluate scalability and practical applications.

Course Curriculum & Resources

  • Fundamentals of support vector machines and quantum kernels
  • Overview of Qiskit’s QSVM implementation

  • Preparing datasets and encoding into quantum states
  • Training QSVM models and optimizing hyperparameters

  • Comparing QSVM with classical SVM results
  • Evaluating practical use cases and scalability

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Instructor Info

  • Instructor Code: ABM07
  • Specialty: Cloud, Data Science & GenAI Expert
  • Experience: Worked with Microsoft, JP Morgan Chase, Deloitte Consulting in the USA.
  • Education: Holds an MS in Data Analytics Engineering from George Mason University, USA, and an MTech in Biomedical Engineering from IIT Bombay.

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    Frequently Asked Questions

    Basic knowledge of quantum computing and machine learning is helpful but not mandatory.
    Python, Qiskit, and quantum computing cloud platforms like IBM Quantum.
    Yes, the project includes benchmarking QSVM against traditional SVM methods.
    It provides hands-on experience in quantum machine learning and its potential for real-world applications

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