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The study examines the interplay between legal frameworks and political dynamics shaping women’s empowerment in contemporary India. Drawing on constitutional provisions, major statutes, national datasets and illustrative case studies, the study synthesizes legal analysis with political economy and feminist perspectives. It contends that India possesses an extensive formal architecture for gender equality, including constitutional guarantees, targeted legislation and local-level reservations, yet substantive empowerment for many women remains uneven. Main barriers identified are entrenched social norms, weak enforcement mechanisms, economic dependency, and intersectional disadvantages linked to caste, class and region. Evidence from grassroots reservation experiments and community initiatives reveals both promising shifts in public goods provision and persistent challenges such as proxy representation and inadequate party support. The study highlights gaps in protection against gender-based violence, low female labour force participation and regional disparities in access to services. Policy recommendations include timely and inclusive implementation of reservation measures, gender-responsive budgeting, institutional strengthening for law enforcement, expansion of formal economic opportunities, and community-level social-norm interventions. It concludes that legal reform is necessary but insufficient; sustained political will, institutional investment and social transformation are essential to convert formal rights into meaningful freedoms for all women in India. It advocates sustained, coordinated, multi-sectoral action.

The political empowerment of women in India has been a cornerstone of the nation's democratic evolution. While strides have been made through legal reforms and policy initiatives, significant challenges persist. This article delves into the trajectory of women's political participation, examining the legal frameworks that have facilitated progress, the obstacles that hinder full participation, and the implications of recent legislative developments. By analysing the intersection of law, politics, and gender, the paper offers a comprehensive overview of the current landscape and suggests pathways for future advancements.

Quantum computers and simulators promise to enable the study of strongly correlated quantum systems. Yet, surprisingly, it is hard for them to compute ground states. They can, however, efficiently compute the dynamics of closed quantum systems. We propose a method to study the quantum thermodynamics of strongly correlated electrons from quantum dynamics. We define time-averaged classical shadows (TACS) and prove it is a classical shadow(CS) of the von Neumann ensemble, the time-averaged density matrix. We then show that the diffusion maps, an unsupervised machine learning algorithm, can efficiently learn the phase diagram and phase transition of the one-dimensional transverse field Ising model both for ground states using CS \\emph{and state trajectories} using TACS. It does so from state trajectories by learning features that appear to be susceptibility and entropy from a total of 90,000 shots taken along a path in the microcanonical phase diagram. Our results suggest a low number of shots from quantum simulators can produce quantum thermodynamic data with a quantum advantage.