3 papers
quant-ph2026
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…
quant-ph2026
Shallow instantaneous quantum polynomial-time circuits for generative modeling on noisy intermediate-scale quantum hardware
Oriol Balló-Gimbernat, Marcos Arroyo-Sánchez, Paula GarcÃa-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…
quant-ph2026
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
Jesús Lozano-Cruz, Albert Nieto-Morales, Oriol Balló-Gimbernat +3
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to…