Transformer Neural Networks (TNNs) are a type of deep learning architecture that have revolutionized the field of natural language processing (NLP) and beyond. Transformers can be scaled up to handle very large datasets and complex tasks. This scalability has led to the development of large language models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).

As the demand for advanced analytics, ML and AI is growing, these technologies are seen as essential tools for gaining a competitive advantage, driving innovation and steering business decisions. However, TNNs often fall short in conducting advanced analyses on large and sophisticated datasets. The rising demand for real-time data processing and analysis, driven by the growth of the IoT and real-time analytics, is another challenge that TNNs struggle to meet due to their computational demands and inefficiencies.

The Transformer Quantum Neural Network (TQNN) is an emerging concept that combines the principles of quantum computing with transformer neural network architectures. This hybrid approach aims to leverage the strengths of both quantum mechanics and advanced machine learning techniques to solve complex problems more efficiently.

TQNN, which utilizes quantum computing principles, offers a promising solution to these challenges associated with TNNs. This whitepaper explores the challenges TNNs face, the rise of TQNN and the transformative impact it has on computational capabilities. It also provides a comprehensive comparison with the performance metrics of GPU-based TNN.

To learn more about TQNN, download our latest whitepaper.