5 papers
Interferometric Quantum Polynomial Chaos Expansion as a Generative Model for Calorimeter Shower Simulation
Jamal Slim, Saverio Monaco, Florian Rehm +3
We present the quantum polynomial chaos expansion, a generative algorithm in which a single circuit is the entire model, and we use it to learn calorimeter images. In a classical c…
Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits
Saverio Monaco, Jamal Slim, Florian Rehm +2
Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods. By employing Pauli propagation, it is…
Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model
Jamal Slim, Saverio Monaco, Florian Rehm +2
Simulating calorimeter showers is one of the largest computing costs in high-energy physics, and quantum generative models have been proposed as compact alternatives. Their progres…
An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment
Jamal Slim, Saverio Monaco, Florian Rehm +3
The challenge to scaling quantum generative models on near-term hardware is training. Variational circuit Born machines require repeated quantum sampling and are prone to barren pl…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…