HOBOTAN: Efficient Higher Order Binary Optimization Solver with Tensor Networks and PyTorch

TL;DR

HOBOTAN is a scalable, GPU-accelerated solver for higher order binary optimization problems that leverages tensor networks and PyTorch, aiming to improve efficiency and scalability in combinatorial optimization.

Contribution

The paper introduces HOBOTAN, a novel tensor network-based solver for HOBO problems with GPU support, combining batch processing and binary encoding for enhanced efficiency.

Findings

Supports CPU and GPU with PyTorch implementation

Demonstrates significant efficiency improvements in optimization tasks

Shows scalability with multiple GPUs for large problems

Abstract

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.