  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.
