Speaker
Description
A new theoretical framework in Quantum Machine Learning (QML) allows to compare the performances of Quantum and Classical ML models on supervised learning tasks. We assess the performance of a quantum and classic support vector machine for a High Energy Physics dataset: the Higgs tt ̄H(b ̄b) decay dataset, grounding our study in a new theoretical framework based on three metrics: the geometry between the classical and quantum learning spaces, the dimensionality of the feature space, and the complexity of the ML models. Hence, we can exclude those areas where we do not expect any advantage in using quantum models and guide our study through the best parameter configurations. We observe, in a vast parameter region, that the used classical rbf kernel model overtakes the performances of the devised quantum kernels. According to the adopted quantum encoding, the Higgs dataset has been proved to be low dimensional in the quantum feature space. Nevertheless, including a projected quantum kernel, able to reduce the expressivity of the traditional fidelity quantum, a clever optimization of the parameters revealed a potential window of quantum advantage where quantum kernel is able to better classify the Higgs boson events and surpass the classical ML model.
| Consider for long presentation | No |
|---|
