Quantum Machine Learning (QML) for the analysis of data in High Energy Physics (HEP)
About
Research activities are primarily focused on the development of Quantum Machine Learning (QML) algorithms for the analysis of High Energy Physics (HEP) data.
The group is engaged in the development of quantum neural networks for the analysis of data from the Large Hadron Collider (LHC), with a particular focus on boosted topologies and Vector Boson Scattering (VBS) processes. In this context, we have recently introduced the Lund Plane to Bloch (LP2B) Encoding method for jet substructure analysis (preprint: arXiv:2604.18613, currently under publication).
The QuEST (Quantum Experiment and Simulation Theory) project has been selected for funding under the 2025 University Research Fund of University of Perugia. The project aims to develop quantum machine learning and quantum simulation algorithms for High Energy Physics applications.
Team
- Fabio Napolitano Researcher — University of Perugia
- Luca della Penna PhD student — University of Perugia
- Tommaso Tedeschi PostDoc — INFN Perugia
- Livio Fanò Full Professor — University of Perugia/INFN Perugia
Publications
The application of quantum algorithms to jet substructure analysis is of growing interest as NISQ hardware continues to mature in qubit count and gate depth. Jet substructure remains essential for addressing demanding and complementary challenges at the LHC and beyond, notably object classification and polarization tagging. However, existing quantum machine learning approaches typically rely on data representations that suffer from infrared and collinear unsafety, sensitivity to non-perturbative effects, or poor scalability. In this work, we introduce the Lund Plane to Bloch (LP2B) encoding, designed to map a theoretically clean and robust representation of jet kinematics directly into qubit states. Leveraging this encoding, we implement a Quantum Tree-Topology Network (QTTN) that natively embeds the hierarchical structure of the Lund tree. We evaluate the QTTN across multiple benchmarks and observe that it matches the performance of large classical deep learning architectures, such as LundNet, on polarization tagging, while maintaining competitive accuracy for W boson and top quark tagging. The architecture demonstrates enhanced sensitivity compared to standard 1P1Q encodings on both polarization and W tagging, and pushes the Pareto front when compared against MLP of similar size and BDTs. Remarkably, the QTTN requires three orders of magnitude fewer parameters than LundNet, demonstrating promises for low-latency FPGA implementations in trigger systems. Furthermore, the QTTN outperforms classical methods in the low-data regime, making it suitable for low-yield, data-driven analyses. We also find that the quantum model is less susceptible to overfitting generator-specific parton shower and hadronization models than classical deep learning approaches, pointing toward potentially smaller systematic uncertainties. We validate the QTTN on real quantum hardware using a 3-qubit SpinQ device.