Sutirtha Biswas | Searcharxiv

arXiv2025

Efficient population transfer in a quantum dot exciton under phonon-induced decoherence via shortcuts to adiabaticity

In the present study, we apply shortcut to adiabaticity pulses (time-dependent Rabi frequency and detuning) for the efficient population transfer from the ground to the exciton state in a GaAs/InGaAs quantum dot with phonon-induced dephasing. We use the time-evolving matrix product operator (TEMPO) method to propagate system in time and find that, for temperatures below $ 20 \ \text{K} $ and pulse duration up to $ 10 \ \text{ps} $, a very good transfer efficiency is obtained in general. We explain these results using a Bloch-like equation derived from a generalized Lindblad equation, which adequately describes system dynamics at lower temperatures. For higher temperatures, the transfer efficiency is significantly reduced except for subpicosecond pulses, where the shortcut Rabi frequency reduces to a delta pulse attaining a fast population inversion. The present work is expected to find application in quantum technologies which exploit quantum dots for single-photon generation on demand.

cond-mat.mes-hall↗

arXiv2025

A physics-informed U-Net-LSTM network for nonlinear structural response under seismic excitation

Accurate and efficient seismic response prediction is essential for the design of resilient structures. While the Finite Element Method (FEM) remains the standard for nonlinear seismic analysis, its high computational demands limit its scalability and real-time applicability. Recent developments in deep learning - particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models - have shown promise in reducing the computational cost of the nonlinear seismic analysis of structures. However, these data-driven models often struggle to generalize and capture the underlying physics, leading to reduced reliability. We propose a novel Physics-Informed U-Net-LSTM framework that integrates physical laws with deep learning to enhance both accuracy and efficiency. The proposed 1D U-Net captures the underlying latent features of the long-term input sequences. By embedding domain-specific constraints into the learning process, the proposed model achieves improved predictive performance over conventional Machine Learning (ML) architectures. This approach bridges the gap between purely data-driven methods and physics-based modeling, offering a robust and computationally efficient alternative for predicting the seismic response of structures.

cs.LG↗