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Variational Algorithm Design

This course teaches how to write variational algorithms: near-term, hybrid-quantum-classical algorithms that are ideal candidates to achieve quantum advantage. Throughout this course, you'll learn each step in the variational algorithm design workflow, tradeoffs associated with each step, and how to use Qiskit Runtime primitives to optimize for speed and accuracy.

This course is intended for individuals who have some experience with quantum computing and want to learn how to write variational algorithms using Qiskit Runtime Primitives. It is also suitable for quantum computing professionals who want to expand their knowledge and skills in the field of variational algorithms.

At the end of this course, you'll be able to test your skills and earn a badge!

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Variational Algorithms
  • Pre-course Survey
  • Simplified hybrid workflow
  • Variational theorem
  • Summary
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Reference States
  • Default state
  • Classical reference state
  • Quantum reference state
  • Constructing Reference States using template circuits
  • Application-specific reference states
  • Summary
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Ansatze and Variational Forms
  • Parameterized Quantum Circuits
  • Variational Form and Ansatz
  • Heuristic ansatze and trade-offs
  • Problem-specific ansatze
  • Summary
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Cost Functions
  • Primitives
  • Cost functions
  • Measurement strategy: speed versus accuracy
  • Summary
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Optimization Loops
  • Local and Global Optimizers
  • Gradient-Based and Gradient-Free Optimizers
  • Barren Plateaus
  • Summary
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Instances and Extensions
  • Variational Quantum Eigensolver (VQE)
  • Subspace Search VQE (SSVQE)
  • Variational Quantum Deflation (VQD)
  • Quantum Sampling Regression (QSR)
  • Summary
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Examples and Applications
  • Problem definitions
  • Custom VQE
  • Experimenting to improve speed and accuracy
  • VQD example
  • Quantum Chemistry: Ground State and Excited Energy Solver
  • Optimization: Max-Cut
  • Post-Course Survey
  • Summary
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