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.

翻译：本研究提出HOBOTAN——一种面向高阶二进制优化问题的新型求解器。HOBOTAN支持CPU与GPU双平台，其GPU版本基于PyTorch开发，具备快速且可扩展的系统特性。该求解器利用张量网络解决组合优化问题，通过构建映射问题的高阶二进制优化张量，并依需执行张量缩并运算。此外，结合张量优化的批处理技术与基于二进制的整数编码方法，我们显著提升了组合优化的计算效率。未来通过增加GPU数量有望释放更强算力，实现多GPU高效协同以支撑高可扩展性需求。值得注意的是，HOBOTAN的设计框架兼容量子计算范式，这为未来量子计算机应用提供了技术启示。本文详细阐述了HOBOTAN的设计原理、实现方案、性能评估及可扩展性分析，验证了其有效性。