<p>This research presents a new hybrid quantum deep learning model called Q-OptBERT aimed at accomplishing sentiment analysis through quantum optimization. The solution combines DistilBERT to extract contextual embedding with a Variational Quantum Circuit (VQC) capable of capturing nonlinear linguistic dependencies by utilizing quantum superposition and entanglement as principles of quantum physics. A new Adaptive Quantum Feature Reduction (AQFR) module will allow for dimensional reduction while preserving semantic richness in embedding space. The primary contribution of the research is the introduction of the Quantum Tuned Tunicate Swarm Algorithm (QT-TSA), a biologically-inspired optimization approach to enable faster convergence and diversity of solutions when tuning hyperparameters. This novel optimization process differs fundamentally from other optimization methods such as Particle Swarm Optimization (PSO) or Quantum Genetic Algorithm (QGA) because QT-TSA can dynamically assess and update its own search parameters through the use of quantum potential functions to robustly and systematically explore parameter space. The combined quantum swim method creates an optimization strategy that optimizes the learning rate and entanglement strength of the VQC layer. The experimental results on relevant benchmark datasets show superior accuracy, precision, and less computational time than other Bidirectional Encoder Representations from Transformers (BERT) or hybrid quantum NLP models. Q-OptBERT is a promising next-generation hybrid quantum deep learning model for effective, comprehensible, and high-performance quantum-enhanced natural language processing because of its unique blend of quantum circuit meta-learning, feature adaptation, and swarm optimization.</p>