http://hdl.handle.net/2289/148 Sun, 19 Apr 2026 05:56:51 GMT 2026-04-19T05:56:51Z http://hdl.handle.net/2289/8705 Title: Influence of micro and nanostructures on emission properties of nanosecond laser produced plasma Authors: Varghese, Nikhil; Nideesh, P K; Nivas, Jijil JJ; Amoruso, Salvatore; Smijesh, N Abstract: Emission characteristics of nanosecond laser produced plasma (LPP) from structured and pristine silicon (Si) targets were investigated to ascertain laser-target energy coupling and its impact on the number of effective laser pulses that generate structures. Two-dimensional Fast Fourier Transform (2D-FFT) analysis of scanning electron microscopy (SEM) images quantified the surface structural features of the targets. Emissions from neutrals and ions generated from structured targets were approximately three times higher than those from pristine targets, significantly influenced by the micro and nano structures formed at varying numbers of effective pulses, corresponding to different beam scanning velocities. The most intense emissions were observed for the target with highly ordered laser-induced periodic surface structures (LIPSS), thereby enhancing the laser-target coupling efficiency. Description: Opn Access. Fri, 27 Mar 2026 00:00:00 GMT http://hdl.handle.net/2289/8705 2026-03-27T00:00:00Z http://hdl.handle.net/2289/8695 Title: Certified random number generation using quantum computers Authors: Nath, Pingal Pratyush; Sinha, Aninda; Sinha, Urbasi Abstract: We investigate how current noisy quantum computers can be leveraged for generating secure random numbers certified by Quantum Mechanics. While random numbers can be generated and certified in a device-independent manner through the violation of Bell’s inequality, this method requires significant spatial separation to satisfy the no-signaling condition, making it impractical for implementation on a single quantum computer. Instead, we employ temporal correlations to generate randomness by violating the Leggett-Garg inequality, which relies on the No-Signaling in Time condition to certify randomness, thus overcoming spatial constraints. By applying this protocol to different IBMQ platforms, we demonstrate the feasibility of secure, semi-device-independent random number generation using low-depth circuits with single-qubit gates. We show how error mitigation techniques lead to LGI violation compatible with theoretical predictions on the existing IBMQ machines. Description: Open Access. Also available at arXiv.org (one of the alternative locations) Fri, 19 Sep 2025 00:00:00 GMT http://hdl.handle.net/2289/8695 2025-09-19T00:00:00Z http://hdl.handle.net/2289/8684 Title: LIBS of BaSrTiO3 compounds using ANN based on a stationary state plasma model Authors: Amogh, M S; Xavier, Sebin Sebastian; Jose, Jeena Rose; Philip, Reji; Biju, P R Abstract: Laser Induced Breakdown Spectroscopy (LIBS) is widely used for rapid material characterization and quantitative analysis due to its versatility and speed. Linking LIBS spectra to precise quantitative measurements is challenging because of nonlinear effects arising from surface variations, matrix interactions, and self-absorption. As a result, precise quantitative analysis has been a persistent challenge for the LIBS community. We perform stoichiometric evaluation of BaSrTiO₃ (BST) samples with varying compositions using a stationary-state plasma approach. BST is notable for its high dielectric performance and is employed in applications such as non-volatile memories, pyroelectric sensors, and electro-optic devices. We simulate the LIBS spectra using a two-zone plasma model under Local Thermodynamic Equilibrium (LTE) and fit them to experimental spectra to extract stoichiometry, electron density, and plasma temperature. A Controlled Random Search (CRS) algorithm is used to optimize the fit to find the stoichiometry accurately. Further, an Artificial Neural Network (ANN) is trained exclusively on synthetic spectra representing the constituent elements, and when applied to experimental spectra, it accurately predicts the stoichiometry of samples not seen during training. To our knowledge, this work is the first to employ a two-zone stationary-state plasma model to generate synthetic training data for an ANN, which is then successfully applied to predict elemental compositions from experimental spectra. Our findings show that a simple ANN based on one-dimensional plasma modelling enables rapid in-situ classification and stoichiometric analysis, reducing reliance on large experimental spectral datasets for machine learning. This approach could simplify the complexities associated with experimental spectral requirements for training and predicting elemental concentration using ML models. Description: Restricted Access. Wed, 01 Apr 2026 00:00:00 GMT http://hdl.handle.net/2289/8684 2026-04-01T00:00:00Z http://hdl.handle.net/2289/8681 Title: Quantum technologies with Rydberg atoms Authors: Barik, Shovan Kanti; Thakur, Aishwarya; Jindal, Yashica; Silpa, B S; Roy, Sanjukta Abstract: Rydberg atoms have highly controllable exotic properties such as strong inter-atomic interaction, high polarizability, and long lifetimes which enabled unprecedented progress in Rydberg atom-based quantum Technologies. We present a brief review of recent progress in the development of quantum technologies using Rydberg atoms. We highlight the recent advances in the various regimes of quantum technologies such as quantum Information processing, quantum sensing, quantum simulation of many-body physics and single-photon sources for quantum communications. Description: Open Access Fri, 12 Jul 2024 00:00:00 GMT http://hdl.handle.net/2289/8681 2024-07-12T00:00:00Z