6 papers
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…
Quantum machine learning framework for longitudinal biomedical studies
Maria Demidik, Filippo Utro, Alexey Galda +3
Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders…
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…
How quantum computing can enhance biomarker discovery
Frederik F. Flöther, Daniel Blankenberg, Maria Demidik +7
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very…
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…
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…