Enhanced Quantum-Inspired Neural Architecture Search with Adaptive Pre-trained Backbone Integration - Ardila, Diego Páez; Carvalho, Thiago Medeiros; Vasquez, Santiago; Cardoso, Fabio; Figueiredo, Karla; Vellasco, Marley (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2025: Anais da XXXV Brazilian Conference on Intelligent Systems, 2025)
Neural Architecture Search (NAS) methods have become essential for automating the design of deep neural networks. However, their high computational cost, particularly when training architectures from scratch, remains a significant limitation. This paper proposes an enhancement to the Quantum-Inspired Neural Architecture Search (Q-NAS) algorithm by integrating a dynamically adjustable pre-trained backbone into the search process. The proportion of backbone depth used is jointly optimized with the design of additional building blocks selected from a flexible set of operators. Experiments conducted on four MedMNIST datasets show that the proposed method improves search efficiency, reducing GPU time by up to 66% compared to the baseline. Furthermore, it achieves competitive or superior classification accuracy. Notably, on the PathMNIST dataset, the enhanced QNAS achieved a 3.45% improvement in accuracy over the baseline. These findings demonstrate the effectiveness of combining partial backbone utilization with evolutionary NAS techniques for efficient medical image classification.