1 citations · 1 across the 4 of their papers we have counts for
5 papers
Diagnosing quantum reservoirs at scale based on expressivity and coverage
Laia Domingo, Oriol Balló-Gimbernat, Fernando Vilariño
Quantum reservoirs offer a hardware-friendly route to quantum machine learning, replacing trainable circuits with fixed random dynamics and a classical readout. Because the reservo…
Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis
Yeray Cordero, Paula García-Molina, Fernando Vilariño
Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction, yet classical networks suffer from a well-known s…
Shallow instantaneous quantum polynomial-time circuits for generative modeling on noisy intermediate-scale quantum hardware
Oriol Balló-Gimbernat, Marcos Arroyo-Sánchez, Paula García-Molina +2
Generative modeling is one of the most promising applications of quantum machine learning, yet training and deploying Quantum Generative Models (QGMs) on near-term hardware remains…
CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models
Ayan Banerjee, Fernando Vilariño, Josep Lladós
Preserving facial identity under extreme stylistic transformation remains a major challenge in generative art. In graffiti, a high-contrast, abstract medium, subtle distortions to…
Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches
Y. Cordero, S. Biswas, F. Vilariño +1
Quantum machine learning (QML) investigates how quantum phenomena can be exploited in order to learn data in an alternative way, \textit{e.g.} by means of a quantum computer. While…