FPGA acceleration of tensor network computing for quantum spin models

Increasing the degree of freedom for quantum entanglement within tensor networks can enhance the depiction of the essence in many-body systems. However, this enhancement comes with a significant increase in computational complexity and critical slowing down, which drastically increases time consumption. This work converts a quantum tensor network algorithm into a classical circuit on the Field Programmable Gate Arrays (FPGAs) and arranges the computing unit with a dense parallel design, efficiently optimizing the time consumption. Test results show that the FPGA-based design achieves a computational speed 1.7 times greater than that of the central processing unit and is comparable to the graphics processing unit. This work explores a scalable and reusable approach suitable for parallel tensor operations implemented on FPGA, advancing research in quantum physics for many-body computing and quantum technologies.