41 citations · 41 across the 1 of their papers we have counts for
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Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate 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…
Quantum anomaly detection in the latent space of proton collision events at the LHC
Vasilis Belis, Kinga Anna Woźniak, Ema Puljak +7
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along w…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
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…
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…