Transformers have become the dominant architecture for sequence modeling in natural language processing; however, their effectiveness critically depends on how positional information is encoded. Conventional positional encodings, while effective, may have limited structural flexibility for capturing complex global sequence relationships. Recent quantum-inspired approaches have sought to address this limitation, yet many either oversimplify quantum principles or introduce substantial computational or hardware overhead. We introduce a novel Quantum Fourier Transform (QFT)-inspired positional encoding scheme for transformers, motivated by the structured frequency representation of the QFT. Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions, the proposed method provides a learnable hybrid encoding that preserves quantum-inspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations. Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity, improved robustness to input scrambling, and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings. Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives. These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing (NLP).

Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification accuracy. In this work, we apply transfer learning to a Nasnet-Mobile CNN model to extract deep features and augment it with a novel nature-inspired feature selection algorithm called Mutated Binary Artificial Bee Colony. The selected features are fed to multiple classifiers for final classification. We use $P{H}^2$, ISIC-2016, and HAM $10000$ datasets for experimentation, supported by Monte Carlo simulations for thoroughly evaluating the proposed feature selection mechanism. We carry out a detailed comparison with various benchmark works in terms of convergence rate, accuracy histogram, and reduction percentage histogram, where our method reports 99.15% (2-class) and 97.5% (3-class) accuracy on the $P{H}^2$ dataset, while 96.12% and 94.1% accuracy for the other two datasets, respectively, against minimal features.