Many-body computing on Field Programmable Gate Arrays
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| Publicado en: | Communications Physics vol. 8, no. 1 (2025), p. 117 |
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| Publicado: |
Nature Publishing Group
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | 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(1)<inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="42005_2025_2050_Article_IEq1.gif" /> 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. |
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| ISSN: | 2399-3650 |
| DOI: | 10.1038/s42005-025-02050-z |
| Fuente: | Science Database |