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Niladri Chakraborty

Publications and source records attributed to Niladri Chakraborty.

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Non-Hermitian topology in driven-dissipative systems: correspondence with quantum correlations and a resource for entanglement

Directional amplification, in which signals are amplified selectively depending on their propagation direction, is a key resource for quantum information processing and stands in one-to-one correspondence with non-trivial non-Hermitian topology. So far, this correspondence has concerned the mean fields, and thus classical response. Here we turn to the quantum fluctuations, giving access to correlations and entanglement. For phase-preserving amplifiers, we derive analytic expressions for the normal and anomalous correlations, showing that non-trivial topology produces correlations that grow exponentially with the distance between modes and approach the largest values compatible with the uncertainty relations. The associated correlation length diverges at the topological phase transition. Entanglement nonetheless remains local, set by the competition between normalised anomalous correlations and the asymmetry of the mode occupations. For the bosonic Kitaev chain, a phase-sensitive amplifier, the system instead splits into two halves that are internally fully correlated yet mutually uncorrelated. Our work prepares the ground for exploring the quantum properties of non-Hermitian topological systems with state-of-the-art platforms such as cavity optomechanics and superconducting circuits.

cond-mat.mes-hall

A distribution-free change-point monitoring scheme in high-dimensional settings with application to industrial image surveillance

Existing monitoring tools for multivariate data are often asymptotically distribution-free, computationally intensive, or require a large stretch of stable data. Many of these methods are not applicable to 'high dimension, low sample size' scenarios. With rapid technological advancement, high-dimensional data has become omnipresent in industrial applications. We propose a distribution-free change point monitoring method applicable to high dimensional data. Through an extensive simulation study, performance comparison has been done for different parameter values, under different multivariate distributions with complex dependence structures. The proposed method is robust and efficient in detecting change points under a wide range of shifts in the process distribution. A real-life application illustrated with the help of high-dimensional image surveillance dataset.

stat.ME