satishbabu0/Quantum-Machine-Learning-Research

Hybrid Quantum Machine Learning framework using BUQRNN and Multilingual BERT for low-resource language processing and intelligent AI systems. # Design and Development of Quantum Machine Learning --- # 🚀 Overview This project explores how Quantum Machine Learning (QML) can improve Natural Language Processing (NLP), especially for low-resource languages where training data is limited. The proposed model, **BUQRNN (Batched Upload Quantum Recurrent Neural Network)**, combines classical AI with quantum computing concepts to create a smarter and more efficient learning system. The framework combines: - 🌐 Multilingual BERT for contextual text understanding - ⚛️ Quantum Recurrent Neural Networks - 🔄 Variational Quantum Circuits - 📦 Batched Upload Encoding - 🤖 Hybrid Quantum-Classical Learning The goal is to improve: - NLP classification accuracy - Feature extraction - Computational efficiency - Low-resource language understanding --- # 🌐 Live Project & Repository ## 🔗 Live Deployed Website Quantum Machine Learning Research Website ## 💻 GitHub Repository Quantum-Machine-Learning-Research Repository --- # 📊 System Architecture ```mermaid graph TD A[Input Text] --> B[Multilingual BERT Embeddings] B --> C[Batched Upload Quantum Encoder] C --> D[Parameterized Quantum Circuit] D --> E[Quantum Measurement] E --> F[Quantum Recurrent Processing] F --> G[Fully Connected Layer] G --> H[Output Classification] ``` --- # 🎯 Objectives of the Research - Build an efficient hybrid Quantum Machine Learning architecture - Improve multilingual NLP performance - Reduce information loss during quantum encoding - Improve scalability using batched upload techniques - Explore real-world applications of Quantum AI systems --- # 🛠️ Technologies Used | Technology | Usage | |---|---| | Python | Programming Language | | PyTorch | Deep Learning Framework | | PennyLane | Quantum Machine Learning | | Multilingual BERT | NLP Embeddings | | Quantum Computing | Quantum Processing | | NLP | Text Analysis | --- # ⚙️ Proposed BUQRNN Architecture The proposed system contains: 1. Input Embedding Layer 2. Batched Upload Quan …