Developed ArmInductorQuantizer support for Pt2e quantization on ARM within the pytorch/ao repository, focusing on enhancing model performance for ARM architectures. The work involved implementing quantization features specifically for conv2d and linear layers, accompanied by comprehensive unit tests to ensure correctness and reliability. Addressed continuous integration issues and improved code style adherence, contributing to more robust automated validation processes. Expanded test coverage for various Pt2e quantization scenarios and laid the groundwork for broader hardware deployment. Utilized Python and PyTorch, applying machine learning and quantization expertise to deliver a targeted feature that supports future scalability and maintainability in ARM environments.