Abstract Quantum information scrambling (QIS), from the perspective of quantum theory, is generally understood as local non-retrievability evolved through some dynamical process, and often quantified via entropic quantities such tripartite information. We argue that this approach comes with a number issues, in large part due to its reliance on mutual informations, which do not faithfully quanti...

A bstract In semi-classical systems, the exponential growth of out-of-time-order correlator (OTOC) is believed to be hallmark quantum chaos. However, on several occasions, it has been argued that, even in integrable OTOC can grow exponentially due presence unstable saddle points phase space. this work, we probe such an system exhibiting saddle-dominated scrambling through Krylov complexity and ...

An analysis of some recently proposed methods of digital image scrambling that use Fibonacci numbers. This report describes the mathematics required to understand why these methods work.

A bstract We quantify the role of scrambling in quantum machine learning. characterize a neural network’s (QNNs) error terms properties via out-of-time-ordered correlator (OTOC). network can be trained by minimizing loss function. show that function bounded OTOC. prove gradient This demonstrates OTOC landscape regulates trainability QNN. numerically this is flat for maximally QNNs, which pose c...

1. Introduction The position in which an argument is projected is usually assumed to be determined strictly by conditions of the computational system (X-bar theory) and the LF interface (Θ-theory). We will argue that these systems, although they obviously restrict the distribution of arguments, still allow in many cases a range of positions in which a given argument can be generated (merged). I...

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