1 citations · 1 across the 4 of their papers we have counts for
6 papers
Classical training of conditional instantaneous quantum polynomial-time Born machines
Oriol Balló-Gimbernat, Emiliano Tolotti, Laia Domingo +2
Instantaneous quantum polynomial-time (IQP) Born machines are attractive quantum generative models, as certain IQP circuit families are conjectured to be hard to sample, yet they c…
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…