7 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Using reservoir computing to construct scarred wavefunctions
L. Domingo, J. Borondo, F. Borondo
Scar theory is one of the fundamental pillars in the field of quantum chaos, and scarred functions a superb tool to carry out studies in it. Several methods, usually semiclassical,…
Optimal quantum reservoir computing for market forecasting: An application to fight food price crises
L. Domingo, M. Grande, G. Carlo +2
The emerging technology of quantum reservoir computing (QRC) stands out in the noisy-intermediate scale quantum era (NISQ) for its exceptional efficiency and adaptability. By harne…
Quantum reservoir complexity by Krylov evolution approach
Laia Domingo, F. Borondo, Gastón Scialchi +3
Quantum reservoir computing algorithms recently emerged as a standout approach in the development of successful methods for the NISQ era, because of its superb performance and comp…
Optimal quantum reservoir computing for the NISQ era
L. Domingo, G. Carlo, F. Borondo
Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computatio…
Adapting reservoir computing to solve the Schrödinger equation
L. Domingo, J. Borondo, F. Borondo
Reservoir computing is a machine learning algorithm that excels at predicting the evolution of time series, in particular, dynamical systems. Moreover, it has also shown superb per…