Generic non-Markovian quantum processes have infinitely long memory, implying an exact description that grows exponentially in complexity with observation time. Here, we present a finite memory ansatz approximates (or recovers) the true process errors bounded by strength of memory. The introduced is operational quantity and depends on way probed. Remarkably, recovery error smallest over all pos...

Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dynamics depend on the current state only, and not on previous history. Non-Markovian decision processes (NMDPs) can also be defined, but then the more tractable solution techniques developed for MDP’s cannot be directly...

Most noise-characterization methods for quantum technologies assume Markovianity, meaning that the environment and system have no memory of their interactions with each other, because it is inefficient computationally demanding to take temporal correlations into account. Here, authors propose a more efficient machine learning method estimating non-Markovian noise implement in proof-of-principle...

Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dynamics depend on the current state only, and not on previous history. Non-Markovian decision processes (NMDPs) can also be defined, but then the more tractable solution techniques developed for MDP’s cannot be directly...

In this article we prove the validity of the Dellman Optimality Equa­ tion a.nd related results for sequential decision problems with a general recursive structure. The characteristic feature of our approach is that also non-Markovian policies are taken into account. The theory is moti­ vated by some experiments with a learning robot.