This scientific working group focuses on the implementation of tensor network states and neural network algorithms on specialized hardware platforms, including FPGAs (Field Programmable Gate Arrays) and GPUs (Graphics Processing Units). The group aims to explore innovative approaches to accelerate computations in quantum many-body systems, quantum field theory simulations, and optimization problems.
Key objectives include:
- Developing hardware-optimized algorithms for tensor contractions and neural network inference/training.
- Leveraging the parallel processing capabilities of GPUs and the reconfigurability of FPGAs to enhance performance for large-scale simulations.
- Addressing challenges such as memory bandwidth limitations, precision trade-offs, and energy efficiency in high-performance computing contexts.
By integrating expertise from quantum information science, machine learning, and hardware acceleration, the group seeks to bridge theoretical developments with practical implementations, enabling breakthroughs in scalable simulations and real-time quantum data processing.
