8 citations · 8 across the 5 of their papers we have counts for
8 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…
An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment
Jamal Slim, Saverio Monaco, Florian Rehm +2
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…
Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model
Jamal Slim, Saverio Monaco, Florian Rehm +2
Gate-based quantum generative models have been limited to small images, because their quantum resources grow with the size of the data. The Quantum Feature Amplification Network (Q…
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…
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…
CaloPointFlow II Generating Calorimeter Showers as Point Clouds
Simon Schnake, Dirk Krücker, Kerstin Borras
The simulation of calorimeter showers presents a significant computational challenge, impacting the efficiency and accuracy of particle physics experiments. While generative ML mod…
DeepTreeGANv2: Iterative Pooling of Point Clouds
Moritz Alfons Wilhelm Scham, Dirk Krücker, Kerstin Borras
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generati…
DeepTreeGAN: Fast Generation of High Dimensional Point Clouds
Moritz Alfons Wilhelm Scham, Dirk Krücker, Benno Käch +1
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generati…