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Optimization
Enrollment in this course is by invitation only
A course that provides in-depth knowledge about quantum algorithms used for optimization.
About this course
This course explores the existing quantum algorithms for optimization. It is developed for an audience of technical professionals. After presenting the general optimization framework and the classical algorithms for optimization, the course provides detailed discussion of variational algorithms, targeted towards current generation of quantum computers, and universal algorithms, designed for fault tolerant quantum computers.
The discussion of each algorithm is accompanied by Python notebooks that demonstrate the use of the algorithm on small size problems using the Qiskit libraries. Finally, an industry relevant use case is discussed together with a hybrid approach to solve the related optimization problem.
Prerequisites
To succeed in this course, you should ideally first complete the Quantum technical foundations course and a good understanding of the following:
- Basic optimization concepts: Components of an optimization problem, such as objective function, constraints, domains etc.
- Calculus: Single and multi variable Differentiation
- Python: Write and maintain reliable code; NumPy or SciPy packages
- Optimization software: Working knowledge of modeling with CPLEX (optional)
Technical requirements
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