1.2k citations · 7.5k across the 116 of their papers we have counts for
7 papers · 2 filters
A Study on Quantum Graph Neural Networks Applied to Molecular Physics
Simone Piperno, Andrea Ceschini, Su Yeon Chang +3
This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approa…
Order Parameter Discovery for Quantum Many-Body Systems
Nicola Mariella, Tara Murphy, Francesco Di Marcantonio +4
Quantum phase transitions reveal deep insights into the behavior of many-body quantum systems, but identifying these transitions without prior knowledge of order parameters remains…
Latent Style-based Quantum GAN for high-quality Image Generation
Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux +2
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images…
Operational Framework for a Quantum Database
Carla Rieger, Michele Grossi, Gian Giacomo Guerreschi +2
Databases are an essential component of modern computing infrastructures and allow efficient manipulation of inherently structured data. The structure depends on the type and relat…
Parameterized quantum circuits as universal generative models for continuous multivariate distributions
Alice Barthe, Michele Grossi, Sofia Vallecorsa +2
Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, the…
Guided Quantum Compression for High Dimensional Data Classification
Vasilis Belis, Patrick Odagiu, Michele Grossi +3
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers…
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…