Qounselor provides Master’s-level academic training dedicated to discovering the applications of quantum computers and critically analyzing their performance.
The objective of this course is to introduce different quantum solution methods for combinatorial optimization problems:
The course emphasizes comparing results obtained with quantum machines and classical methods to critically analyze the performance and limitations of current quantum algorithms and hardware platforms.
The program covers the formulation of QUBO (unconstrained quadratic binary optimization) problems, as well as selected methods from classical combinatorics, including heuristics such as simulated annealing and Tabu search, and deterministic methods such as branch and bound. Quantum combinatorial methods are then introduced, including adiabatic computing, quantum annealing, variational algorithms such as QAOA, and Grover’s algorithm.
The sessions combine theoretical content, exercises, and practical computer-based implementations, with experiments on emulators, simulators, and real quantum hardware.
The course is project-oriented. Students progressively build a library of classical and quantum algorithms for solving combinatorial optimization problems. This approach revisits theoretical concepts through practice and identifies the limitations of current quantum computers, such as noise and compilation costs.
By the end of the course, students are able to formulate an optimization problem as a QUBO, implement several classical and quantum methods to solve this problem, and critically analyze their performance. The course, therefore, aims to train profiles capable of concretely assessing the relevance of quantum technologies for applied use cases.
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