26 citations · 48 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Expressive equivalence of classical and quantum restricted Boltzmann machines
Maria Demidik, Cenk Tüysüz, Nico Piatkowski +2
Quantum computers offer the potential for efficiently sampling from complex probability distributions, attracting increasing interest in generative modeling within quantum machine…
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…
Imaginary Hamiltonian variational ansatz for combinatorial optimization problems
Xiaoyang Wang, Yahui Chai, Xu Feng +3
Obtaining exact solutions to combinatorial optimization problems using classical computing is computationally expensive. The current tenet in the field is that quantum computers ca…
Symmetry breaking in geometric quantum machine learning in the presence of noise
Cenk Tüysüz, Su Yeon Chang, Maria Demidik +3
Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouragi…