Speaker
Description
Quantum Computing and Machine Learning are both significant and appealing research fields. In particular, the combination of both has led to the emergence of the research field of quantum machine learning which has recently taken enormous popularity. We investigate in the potential advantages of this synergy for the application in high energy physics, more precisely in the reconstruction of particle decay trees in particle collision experiments. Due to the larger computational space of quantum computers, this highly complex combinatorical problem is well suited for investigating in a potential quantum advantage compared to the classical scenario. However, current quantum devices are subject to noise and provide only a limited number of qubits. We therefore propose the utilization of a variational quantum circuit within a classical graph neural network which has been shown to be feasible for reconstruction of particle decay trees before. We evaluate our approach on artificially generated decay trees on a quantum simulator and a real quantum computer by IBM Quantum and compare our results to the purely classical approach. Our proposed approach does not only enable the effective utilization of nowadays quantum devices, but also shows competitive results even in the presence of noise.
Significance
The presentation will show the results of our investigations of using variational quantum circuits in graph neural networks under noisy conditions for reconstructing particle decay trees.
