From
questions
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systems.

Most of the traditional Applicant Tracking Systems (ATS) depend on strict matching using keywords, where candidates that are highly qualified are many times disqualified because of minor semantic differences. In this article, the two-stage process of developing a more comprehensive resume assessment system based on small language model that is trained with fewer than 600M parameters is introduced and fine-tuned by using GRPO with a unique-designed reward function. The initial stage is (SFT) Supervised Fine Tuning, which are use to create a strong base model with the ability to perceive resumes beyond superficial overlap of keywords. This SFT model is further-optimized in the second step with Reinforced Learning (RL) via GRPO with the help of multi-component based rewarding, which will not be considered as a commission of tokens matching. In the initial RL experiments, we found a severe difficulty in the shape of reward hacking: overly aggressive penalty terms resulted in unstable training dynamics and prohibitively negative model behaviour. This was solved by trial and error refinement of the reward, and careful training hyperparameter tuning, which led to a stable and controlled process of gentle polishing. GRPO-refined model shows high real-life performance, as it shows accuracy of 91% on unseen data used for testing. It has a high recall of 0.85 on the SELECTED class with a perfect precision of 1.0, which highlights its high reliability to be used in identifying qualified applicants. These findings demonstrate that an appropriately structured two-step fine-tuning pipeline can effectively be used to transfer a small language model into human-like candidate evaluation, surpassing shortcoming of both traditional ATS systems and unrefined uses of reinforcement learning.

Recent advancements in smart computing and intelligent decision communication systems have enabled new possibilities for sustainable financial technologies. This chapter introduces a hybrid quantum classical model for credit risk prediction that leverages superposition and entanglement to enhance complex financial data processing. Unlike traditional machine learning models that struggle with class imbalance, nonlinear relationships and high dimensional dependencies, the system combines advanced preprocessing, feature selection and quantum kernel computation within a scalable support vector framework. Trained on 33,000 loan records across 12 borrower attributes, it achieves 94.53% accuracy, outperforming logistic regression, random forests, decision trees and CNNs. By improving decision accuracy and reducing default risk, the approach contributes to economic sustainability and responsible lending. The same quantum communication architecture can extend to healthcare, logistics and cybersecurity, aligning with the wider vision of smart technology for a sustainable future.

Processing digital media such images is a crucial step for any image classification task such as surface defect detection or any deep learning task which include Digital Media Processing. Utilising quantum computing for digital image processing gives an advantage over classical methods as it operates on high dimensional space and leverages quantum parallelism to explore numerous possibilities simultaneously.