8 citations · 8 across the 5 of their papers we have counts for
4 papers · 1 filter
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…