32 citations · 47 across the 9 of their papers we have counts for
12 papers
Quantum Fourier Generative Models Trainable at Large Scale
Cenk Tüysüz, Oleksandr Kyriienko, Michele Grossi
We propose an algorithmic framework for building and training quantum generative models corresponding to multivariate probability distributions. Our model uses parallel Fourier fea…
Physics inspired quantum algorithm for QCD splitting functions
Gabriel Rouxinol, Yacine Haddad, Cenk Tüysüz +2
We introduce a modular quantum circuit primitive to model entanglement dynamics in QCD parton splitting and use it as a composable building block for data-driven, physics-consisten…
Sample-based training of quantum generative models
Maria Demidik, Cenk Tüysüz, Michele Grossi +1
Quantum computers can efficiently sample from probability distributions that are believed to be classically intractable, providing a foundation for quantum generative modeling. How…
Learning response functions of analog quantum computers: analysis of neutral-atom and superconducting platforms
Cenk Tüysüz, Abhijith Jayakumar, Carleton Coffrin +2
Analog quantum computation is an attractive paradigm for the simulation of time-dependent quantum systems. Programmable analog quantum computers have been realized in hardware usin…
Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Cenk Tüysüz, Maria Demidik, Luuk Coopmans +5
In recent years, researchers have been exploring ways to generalize Boltzmann machines (BMs) to quantum systems, leading to the development of variations such as fully-visible and…
Symmetry enhanced variational quantum imaginary time evolution
Xiaoyang Wang, Yahui Chai, Maria Demidik +3
The variational quantum imaginary time evolution (VarQITE) algorithm is a near-term method to prepare the ground state and Gibbs state of Hamiltonians. Finding an appropriate param…
Particle track reconstruction with noisy intermediate-scale quantum computers
Tim Schwägerl, Cigdem Issever, Karl Jansen +4
The reconstruction of trajectories of charged particles is a key computational challenge for current and future collider experiments. Considering the rapid progress in quantum comp…
Track reconstruction at the LUXE experiment using quantum algorithms
Arianna Crippa, Lena Funcke, Tobias Hartung +8
LUXE (Laser Und XFEL Experiment) is a proposed experiment at DESY which will study Quantum Electrodynamics (QED) in the strong-field regime, where QED becomes non-perturbative. Mea…
New Directions in Quantum Music: concepts for a quantum keyboard and the sound of the Ising model
Giuseppe Clemente, Arianna Crippa, Karl Jansen +1
We explore ideas for generating sounds and eventually music by using quantum devices in the NISQ era using quantum circuits. In particular, we first consider a concept for a "qeybo…
Performance of Particle Tracking Using a Quantum Graph Neural Network
Cenk Tüysüz, Kristiane Novotny, Carla Rieger +7
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminos…
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
Cenk Tüysüz, Federico Carminati, Bilge Demirköz +6
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experim…
Particle Track Reconstruction with Quantum Algorithms
Cenk Tüysüz, Federico Carminati, Bilge Demirköz +6
Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increa…