83 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…
Search for a new neutral gauge boson produced in association with one or two b jets and decaying into a pair of muons in proton-proton collisions at = 13 TeV
CMS Collaboration
A search for a new neutral gauge boson, Z', produced in association with one or two jets, including at least one b jet, and decaying into a pair of muons is presented. The analysis…
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…
Search for dark matter production in association with bottom quarks and a lepton pair in proton-proton collisions at = 13 TeV
CMS Collaboration
A search is performed for dark matter produced in association with bottom quarks and a pair of electrons or muons in data collected with the CMS detector at the LHC, corresponding…
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…