BERT-Residual Quantum Language Model Inspired by ODE Multi-Step Method

Aim:
To design and develop a hybrid GPT + Quantum-Inspired language model that effectively distinguishes between human-written and AI-generated text using contextual embeddings and quantum-style measurement operators.

Abstract:
This work proposes a novel GPT + Quantum-Inspired model that integrates pretrained transformer embeddings with quantum-mechanical principles for text classification. The system detects whether a given text is authored by a human or generated by an AI model. Text data are tokenized using a GPT-2 tokenizer and passed through the pretrained GPT encoder to obtain deep contextual embeddings. These embeddings are then projected into a quantum-inspired subspace, forming a density-like matrix representation that captures token-level semantic interactions. A set of trainable quantum-style measurement operators extract higher-order semantic distribution features, which are fused with GPT’s mean contextual embedding for final classification. The fusion layer, implemented through a feed-forward neural classifier with dropout and softmax, predicts AI vs Human probabilities. End-to-end training with AdamW optimizer and cross-entropy loss jointly fine-tunes GPT and the quantum measurement parameters. Experimental results demonstrate that the hybrid architecture captures subtle stylistic and semantic cues, providing improved interpretability and accuracy compared with classical transformer-only baselines.

Proposed System:        
The proposed GPT + Quantum-Inspired Hybrid Model introduces an enhanced mechanism for identifying AI-generated text by combining the contextual power of GPT with the mathematical expressiveness of quantum-inspired representation. The GPT encoder provides deep bidirectional contextual embeddings that capture linguistic coherence and token-level dependencies. These embeddings are then projected into a quantum subspace to create a density-like representation that models higher-order semantic correlations. Trainable measurement operators act as quantum observables to extract distributional semantics, enabling the model to understand complex variations in writing style. The outputs from GPT and the quantum measurement modules are fused into a unified feature space and passed through a neural classifier for final prediction. This architecture not only improves classification accuracy but also enhances interpretability through quantum-inspired visualization of semantic responses. The system is trained end-to-end using AdamW optimization and deployed as an interactive Streamlit web application, offering real-time detection of AI-generated content.

Advantage:

• Integrates GPT’s deep contextual semantics with quantum-inspired statistical representation.
• Captures both syntactic and stylistic variations.
• Provides interpretable quantum measurement responses.
• Achieves higher accuracy and robustness over standalone transformer models.
• Supports real-time deployment via lightweight web interface.