Many-body computing on Field Programmable Gate Arrays

Abstract

Improving many-body computational efficiency is crucial for exploring condensed matter systems. However, existing acceleration methods are limited and mostly based on von Neumann-like architectures. Here we leverage the capabilities of Field Programmable Gate Arrays for conducting quantum many-body calculations and realize a tenfold speedup over Central Processing Unit-based computation for a Monte Carlo algorithm. By using a supercell structure and simulating the hardware architecture with High-Level Synthesis, we achieve $O\left(1\right)$ scaling for the time of one sweep, regardless of the overall system size. We also demonstrate the utilization of programmable hardware to accelerate a typical tensor network algorithm for ground-state calculations. Additionally, we show that the current hardware computing acceleration is on par with that of multi-threaded Graphics Processing Unit parallel processing. Our findings highlight the advantages of hardware implementation and pave the way for efficient many-body computations.

This work leverages the capabilities of Field Programmable Gate Arrays (FPGAs) for quantum many-body calculations. By designing appropriate schemes for Monte Carlo and tensor network methods, the authors utilize FPGAs’ parallel processing power and implement hardware acceleration for two algorithms.