Speaker
Description
Simulating electromagnetic particle showers in calorimeters is a key task in HEP. To model these complex, correlated distributions on a quantum circuit, the energy data are reduced to binary or encoded into gate angles. When applied to non-binary distributions, these methods exhibit notable limitations: mapping integer numbers into qubit-compatible binary representations often destroys the original metric structure of the data. Instantaneous Quantum Polynomial (IQP) circuits have been proven useful in quantum generative learning models. Expectation values of IQP circuits can be estimated classically, while sampling remains hard. This means that training can be carried out using classical computing resources, while generation necessitates a quantum device. However, IQP circuits mainly focus on the generation of binary distributions. In our work, we extend parameterized IQP circuits to qudits, developing the framework for addressing the non-binary energy deposits from single-particle electron showers. We design a qudit-based quantum circuit and correspondingly adapt the loss function. The method can be extended to other applications that utilize quantum generative machine learning for non-binary data.
This poster provides an extension of the talk: ‘From encoding fermions to generating particle showers: Parity methods for HEP’ and is based off the preprint arXiv:2606.28236.