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DTSTART:20210709T143000Z
DTEND:20210709T150000Z
DTSTAMP:20210610T120800Z
CREATED:20210610
LAST-MODIFIED:20210616
SUMMARY:Edoardo Tignone (LeithÃ Srl., Italy)

** Quantum Algorithms for Graph Analytics**
DESCRIPTION:Given the current limitations of the quantum hardware, a promising class of quantum algorithms is the one of the heuristic algorithms, which combine classical and quantum computers in order to find the optimal solution of a problem. All the projects that involve working with graphs or that require a subroutine of combinatorial optimization could highly benefit in terms of speedup from these heuristic algorithms. A well-known example of this class of algorithms is the quantum approximate optimization algorithm (QAOA) whose hybrid quantum-classical nature combines the parametrized evolution of a quantum state with a classical optimization routine. Typically, the optimization of the gate parameters in the quantum circuit is performed via simple search strategies or gradient-based methods. In this talk we will explore different optimization methods and compare their performance and success probability.\n
URL:http://mlqx.quantumexcellence.org/index.php/events/edoardo-tignone-leitha/
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