9 citations · 10 across the 6 of their papers we have counts for
8 papers
Quantum Fokker-Planck solvers as trainable generative models
Tariq Mahassen, Marcin Jastrzebski, Oleksandr Kyriienko
We develop and analyze generative models based on quantum solvers of stochastic differential equations. Unlike the common approach based on layered variational quantum circuits wit…
Adaptive Relational Learning on Multi-instance Quantum Data with Photonic Processors
Marcin Jastrzebski, Shang Yu, Raj B. Patel +1
Loading multiple quantum states in parallel into a quantum machine learning (QML) model can unlock learning tasks where key information resides in the \emph{relations} between stat…
Spectral Bias in Variational Quantum Machine Learning
Callum Duffy, Marcin Jastrzebski
In this work, we investigate the phenomenon of spectral bias in quantum machine learning, where, in classical settings, models tend to fit low-frequency components of a target func…
LArTPC hit-based topology classification with quantum machine learning and symmetry
Callum Duffy, Marcin Jastrzebski, Stefano Vergani +5
We present a new approach to separate track-like and shower-like topologies in liquid argon time projection chamber (LArTPC) experiments for neutrino physics using quantum machine…
To reset, or not to reset -- that is the question
György P. Gehér, Marcin Jastrzebski, Earl T. Campbell +1
Whether to reset qubits, or not, during quantum error correction experiments is a question of both foundational and practical importance for quantum computing. Text-book quantum er…
Unsupervised Beyond-Standard-Model Event Discovery at the LHC with a Novel Quantum Autoencoder
Callum Duffy, Mohammad Hassanshah, Marcin Jastrzebski +1
This study explores the potential of unsupervised anomaly detection for identifying physics beyond the Standard Model that may appear at proton collisions at the Large Hadron Colli…
A quantum-enhanced support vector machine for galaxy classification
Mohammad Hassan Hassanshahi, Marcin Jastrzebski, Sarah Malik +1
Galaxy morphology, a key tracer of the evolution of a galaxy's physical structure, has motivated extensive research on machine learning techniques for efficient and accurate galaxy…
Reconstructing charged particle track segments with a quantum-enhanced support vector machine
Philippa Duckett, Gabriel Facini, Marcin Jastrzebski +3
Reconstructing the trajectories of charged particles from the collection of hits they leave in the detectors of collider experiments like those at the Large Hadron Collider (LHC) i…