4 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…
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…
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…