A graybox machine-learning framework is extended to qudits and demonstrated on simulated qutrits, with a local Taylor expansion for interpreting the learned noise dynamics.
Quantifying the rotating-wave approximation of the Dicke model
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abstract
We analytically find quantitative, non-perturbative bounds to the validity of the rotating-wave approximation (RWA) for the multi-atom generalization of the quantum Rabi model: the Dicke model. Precisely, we bound the norm of the difference between the evolutions of states generated by the Dicke model and its rotating-wave approximated counterpart, that is, the Tavis-Cummings model. The intricate role of the parameters of the model in determining the bounds is discussed and compared with numerical results. Our bounds are intrinsically state-dependent and, in particular, capture a nontrivial dependence on the total angular momentum of the initial state; this behaviour also seems to be confirmed by accompanying numerical results.
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Quantum Engineering of Qudits with Interpretable Machine Learning
A graybox machine-learning framework is extended to qudits and demonstrated on simulated qutrits, with a local Taylor expansion for interpreting the learned noise dynamics.