Q-TetoFormer: A New Gene Regulatory Network Prediction Method Based on Quantum Computing and Transformer

Quantum computing has enabled precise simulation in gene regulatory network (GRN) prediction by capturing regulatory relationships at a microscopic level. However, current quantum approaches face significant limitations due to high noise levels and prohibitive training costs, making them challenging to implement effectively. To address these issues, we introduce Q-TetoFormer (Quantum-Tuned Extraction and Transformation Orchestrator Former), a novel framework that merges quantum computing with Transformer architecture to enhance GRN prediction accuracy and efficiency.Q-TetoFormer begins by preprocessing gene data, feeding it through a neural network-embedded quantum circuit. The circuit’s output then flows into TetoFormer, a Transformer-based module that leverages attention mechanisms to reduce quantum noise and enrich feature extraction. The output from TetoFormer subsequently optimizes the quantum circuit, creating a feedback loop that improves model performance iteratively. Extensive experiments on five GRN prediction benchmarks demonstrate that Q-TetoFormer not only achieves superior prediction accuracy over state-of-the-art methods but also significantly reduces training overhead, enhancing its practical utility.