In HEP, data is generated from a fundamentally quantum process. Even after measurement, quantum correlations between the produced particles can be probed by, e.g., studying the angular distribution of their momenta. The angular correlations between the measured momenta of the particles are a direct consequence of the underlying laws encoded in the amplitude, or equivalently in HEP jargon, the matrix element of the physical process. Theoretically, these amplitudes are computed in the context of the Standard Model of Particle Physics using Quantum Field Theory techniques. The common assumption is that, when designed accordingly, quantum models will be able to naturally deduce these inherently quantum correlations in the data leading to a higher model performance with respect to classical models.

Quantum machine learning could become among the most exciting applications of quantum technologies. However, machine learning algorithms are designed to analyse large amounts of data and they can be considerably different from commonly studied computational tasks. In a NISQ perspective, the challenge related to data and problem dimensionality is important, at the same time, the definition of a quantum advantage for QML approaches can be redefined beyond the simple acceleration/speed-up. Despite the difficulty related to training a model to convergence in a quantum setup, QML could exhibit higher representational power with respect to classical models, and therefore present an advantage in terms of training sample size and/or final accuracy, especially in particularly complicated scenarios, such as those related to generative models.

For this reason, several projects have been launched to better understand the application of the quantum approach to machine learning for HEP data processing. Examples include signal/background classification problems (Quantum SVM for Higgs classification, QML algorithms for classification of SUSY events), reinforcement learning for dynamic optimisation of complex systems (Quantum Reinforcement Learning for accelerator beam steering) and quantum generative models for simulation and anomaly detection (Quantum Generative Adversarial Networks for detector simulation, Quantum Generative Models for Earth observation, Quantum Generative Models for ab-initio calculation of lepton-nucleon scattering). 

In the same context, different projects will investigate the optimisation and adaptation of well-known quantum algorithms, such as Grover’s search, to HEP data processing. As an example, the project Quantum Algorithms for Event Reconstruction at the CMS detector (Q-Track), is underway and focuses on accelerating, through QC, the TICL framework for HGCAL event reconstruction.