Research Design Services

We specialize in advanced research design, focusing on neural networks and tensor network mappings.

Model Architecture Design

Develop specialized layers and loss functions that adhere to physical constraints like gauge invariance.

Training and Testing

Validate methods using known solutions, gradually applying them to increasingly complex systems and models.

Explore applications of advanced models to real-world problems, enhancing understanding and practical implementations.

Application Exploration

Proposing quantum-neural hybrid algorithms directly encoding physical constraints into neural network architectures. These contributions will deepen our understanding of internal mechanisms in deep learning, particularly explaining why certain neural network structures can effectively represent highly entangled quantum systems, and how concepts borrowed from physics can enhance AI systems' expressive capabilities. Research results will demonstrate how more powerful computational tools can be created by fusing physics principles with machine learning architectures, potentially guiding new quantum-inspired neural network designs. By bridging quantum field theory and deep learning, this research has potential to simultaneously advance physics and machine learning fields: providing physicists with new simulation tools while offering AI researchers new methods for handling highly structured data. From a practical application perspective, this research may accelerate developments in materials design, quantum computing, and complex system modeling.