HOBOTAN：基于张量网络和PyTorch的高效高阶二元优化求解器
HOBOTAN: Efficient Higher Order Binary Optimization Solver with Tensor Networks and PyTorch
AI总结：
本文提出HOBOTAN，一种基于PyTorch和张量网络的高阶二元优化（HOBO）求解器，通过批处理与二进制编码提升组合优化效率，并具备量子计算应用潜力与多GPU高可扩展性。
摘要：
在本研究中，我们介绍了HOBOTAN，这是一种专为高阶二元优化（HOBO）设计的新型求解器。HOBOTAN同时支持CPU和GPU，其GPU版本基于PyTorch开发，提供了一个快速且可扩展的系统。该求解器利用张量网络来解决组合优化问题，采用HOBO张量对问题进行映射，并根据需要执行张量收缩。此外，通过结合张量优化的批处理和基于二进制的整数编码等技术，我们显著提升了组合优化的效率。未来，通过增加GPU数量有望获取更强大的计算能力，实现多个GPU间的高效协作以获得高可扩展性。此外，HOBOTAN是在量子计算框架内设计的，从而为未来的量子计算机应用提供了见解。本文详细介绍了HOBOTAN的设计、实现、性能评估和可扩展性，证明了其有效性。
英文摘要：
In this study, we introduce HOBOTAN, a new solver designed for Higher Order Binary Optimization (HOBO). HOBOTAN supports both CPU and GPU, with the GPU version developed based on PyTorch, offering a fast and scalable system. This solver utilizes tensor networks to solve combinatorial optimization problems, employing a HOBO tensor that maps the problem and performs tensor contractions as needed. Additionally, by combining techniques such as batch processing for tensor optimization and binary-based integer encoding, we significantly enhance the efficiency of combinatorial optimization. In the future, the utilization of increased GPU numbers is expected to harness greater computational power, enabling efficient collaboration between multiple GPUs for high scalability. Moreover, HOBOTAN is designed within the framework of quantum computing, thus providing insights for future quantum computer applications. This paper details the design, implementation, performance evaluation, and scalability of HOBOTAN, demonstrating its effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
