A generalized quantum regression formula holds exactly for processes with classical memory, and its violation is a detectable witness of quantum memory, with applications to the spin-boson model.
Quantifying information flow in quantum processes
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abstract
We present a framework for quantifying information flow within general quantum processes. For this purpose, we introduce the signaling power of quantum channels and discuss its relevant operational properties. This function supports extensions to higher-order maps, enabling the evaluation of information flow in general quantum causal networks and also processes with indefinite causal order. Furthermore, our results offer a rigorous approach to information dynamics in open systems that applies also in the presence of initial system-environment correlations, and allows for the distinction between classical and quantum information backflow.
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Two-point measurement correlations beyond the quantum regression theorem
A generalized quantum regression formula holds exactly for processes with classical memory, and its violation is a detectable witness of quantum memory, with applications to the spin-boson model.