From the 1 of 7 linked papers with an AI index.
7 papers
Machine-Learning-Empowered Quantum Sensing of the Plaquette Phase in a Three-Level Delta System
Lorenzo Vitale, Shreyasi Mukherjee, Dario Fasone +4
The paper introduces a machine‑learning method using a multi‑layer perceptron to infer the gauge‑invariant plaquette phase of a three‑level Δ system from experimentally measurable…
Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli +2
We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network bla…
Detection of noise correlations in two qubit systems by Machine Learning
Dario Fasone, Shreyasi Mukherjee, Dario Penna +5
We introduce and validate a machine-learning assisted quantum sensing protocol to classify spatial and temporal correlations of classical noise affecting two ultrastrongly coupled…
Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor
Dario Fasone, Shreyasi Mukherjee, Mauro Paternostro +3
We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting qubits a…
Machine Learning-aided Optimal Control of a noisy qubit
Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli +2
We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-i…
A machine learning based approach to the identification of spectral densities in quantum open systems
Jessica Barr, Shreyasi Mukherjee, Alessandro Ferraro +2
We present a machine learning-based approach for characterising the environment that affects the dynamics of an open quantum system. We focus on the case of an exactly solvable spi…