Speaker
Description
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase dramatically.
Conventional algorithms suffer from scaling problems. We are investigating the possibility of using machine learning techniques in combination with quantum computing.
In our work, we represent charged particle tracks as a graph data structure and train a hybrid graph neural network consisting of classical and quantum layers. The aim is to see if we can gain an advantage using quantum technologies.
We report on the state of the art of quantum machine learning frameworks, such as Jax, Pennylane, and IBM Qiskit, eventually using GPUs as accelerators.
Finally, we give an outlook on the expected performance in terms of scalability of accuracy and efficiency.
