Gabriele Iurlaro
Evaluation of a Quantum Kernel for Graph Classification on Neutral Atoms Quantum Computer.
Rel. Bartolomeo Montrucchio, Edoardo Giusto, Giacomo Vitali, Chiara Vercellino. Politecnico di Torino, Master of science program in Computer Engineering, 2024
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Abstract
This work merges two cutting-edge fields of study, Machine Learning and Quantum Computing, combined for solving the graph classification problem. Machine learning is the study of models and algorithms that “learn” through experience to solve problems, mimicking human intelligence. Quantum Computing is an emerging field of study, that overturns the classical information and computation paradigm by incorporating Quantum Mechanics principles. Both technologies have the potential to change the world, and their future development is still unpredictable. However, we are currently in the Noisy Intermediate-Scale Quantum era(NISQ), with quantum computers not able to perform error correction, and a medium number of quantum bits, not enough for quantum advantage.
In this context, the hybrid quantum-classical algorithms have emerged, with the possibility of classical post-processing of the quantum results
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