73 citations · 114 across the 2 of their papers we have counts for
3 papers
Gradient Ascent Pulse Engineering with Feedback
Riccardo Porotti, Vittorio Peano, Florian Marquardt
Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully sol…
Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
Riccardo Porotti, Antoine Essig, Benjamin Huard +1
Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, bu…
Coherent Transport of Quantum States by Deep Reinforcement Learning
Riccardo Porotti, Dario Tamascelli, Marcello Restelli +1
Some problems in physics can be handled only after a suitable \textit{ansatz }solution has been guessed. Such method is therefore resilient to generalization, resulting of limited…