HEP applications using multi-level quantum systems
19th October 2025 | by M. Lucio
The identification of anomalous events – not explained by the Standard Model of particle physics – and the possible discovery of exotic physical phenomena pose significant theoretical, experimental, and computational challenges. It is anticipated that these challenges will increase significantly with the operation of next-generation colliders, such as the High-Luminosity Large Hadron Collider (HL-LHC). At least 140 collisions will be produced each time two particle bunches meet at the heart of the ATLAS and CMS detectors, compared to around 40 collisions at present. Consequently, significant challenges are to be expected in terms of data processing, reconstruction, and analysis. It is within this demanding context that Quantum Computing (QC) emerges as a promising alternative. By leveraging the quantum mechanical principles of interference and superposition, QC offers the potential for complexity, time, and/or energy efficiencies relative to Classical Computing.
Despite the recognized potential of QC and ongoing advances in quantum hardware, executing realistic quantum algorithms on High-Energy Physics (HEP) scenarios remains challenging. This constraint is primarily due to the limited size of quantum circuits that can be reliably processed on current quantum computers before e.g. decoherence errors compromise the statistical robustness of the measurement. While the usual baseline to assess QC is using two-level quantum systems – qubits – other alternatives with more levels – qudits – have the potential of more compact circuits, thus alleviating this problem. In this work, unsupervised anomaly detection methods that do not rely on prior knowledge of the underlying physics models have been explored within the context of 3-level quantum systems, qutrits, in Quantum Machine Learning (QML).
To this end, quantum autoencoders for qutrits have been implemented for quantum simulation. This facilitates a deeper comprehension of multilevel quantum systems and validates their capacity for developing quantum algorithms that are potentially more compact than their two-level counterparts. Using real jet datasets from the CMS detector, our results show comparable performance with respect to the qubit baseline and a higher potential for expressiveness. This opens the door for future applications using qudits, as well as the study of how this expressiveness can be applied to detect not only whether there is an anomaly or not, but also which type of anomaly it is. This work has just been submitted to the ArXiV, details can be found here.
