Curriculum / Quantum Machine Learning

Track 07Pro15 lessons · 2,225 XP

Quantum Machine Learning

Apply quantum computing to machine learning with variational circuits and optimization.

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What you will learn

  1. 1Classical ML RefresherKey machine learning concepts you need for quantum ML.Reading+75 XP
  2. 2Parameterized Quantum CircuitsBuild trainable quantum circuits with tunable gate angles.Code challenge+125 XP
  3. 3Quantum Data Encoding StrategiesBasis, amplitude, and angle encoding, how to get classical data into qubits.Reading+100 XP
  4. 4Data Encoding in PracticeCompare angle, amplitude, and basis encoding in code experiments.Code challenge+150 XP
  5. 5Quantum Feature MapsUnderstand how quantum feature maps encode classical data into quantum states.Reading+75 XP
  6. 6Quantum KernelsQuantum-enhanced similarity measures for classification.Reading+100 XP
  7. 7Hybrid Classical-Quantum ModelsCombine neural networks with quantum circuits.Code challenge+150 XP
  8. 8Variational Quantum EigensolverVQE, the hybrid algorithm for finding molecular ground states.Code challenge+175 XP
  9. 9QAOA Revisited: Variational OptimizationSolve combinatorial optimization problems with quantum circuits.Code challenge+175 XP
  10. 10Barren PlateausThe trainability challenge that limits deep quantum circuits.Reading+100 XP
  11. 11Quantum Transfer LearningCombine classical neural networks with a quantum circuit layer.Code challenge+175 XP
  12. 12Quantum Advantage in ML, What's RealHonest assessment of where quantum ML is today and where it might win.Code challenge+150 XP
  13. 13Scaling Quantum MLUnderstand the challenges of scaling QML: barren plateaus, data loading, and classical simulation limits.Reading+75 XP
  14. 14QML Binary ClassifierBuild a complete variational quantum classifier for a 2-class dataset.Code challenge+250 XP
  15. 15QML CapstoneSolve a real optimization problem with variational quantum methods.Project+350 XP

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