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Quantum State Tomography (QST) is a fundamental tool for quantum signal processing. However, in real noisy quantum devices construction of the state's density matrix via QST can utilize a large amount of resources. Here, we discuss some signal processing techniques that are currently applied to this resource issue, and implement on current quantum chips a modification that can assist in reducing resources. An application of QST to quantum entanglement distillation is provided for further insight.

Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems.

Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems.

Point processes are finding increasing applications in neuroscience, genomics, and social media. But basic modelling properties are little studied. Here we consider a periodic time-varying Poisson model and develop the asymptotic Cramer-Rao bound. We also develop, for the first time, a maximum likelihood algorithm for parameter estimation.

Consider the set of possible observations turned out by geometric and radiometric transformations of an object. This set is generally a manifold in the ambient space of observations. It has been shown [1] that in those cases where the geometric deformations are affine and the radiometric deformations are monotonic, the radiometry invariant universal manifold embedding (RIUME) provides a mapping from the orbit of deformed observations to a single low dimensional linear subspace of Euclidean space.

Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter.