用分支定界法降低QUBO问题的精度需求，提升硬件效率。
Dynamic Range Reduction via Branch-and-Bound
- 基于动态范围设计分支定界算法，量化复杂度以指导精度压缩。
- 在真实量子退火机上验证，显著降低问题求解所需精度。
- 适合需要高效部署的量子计算与低精度硬件开发者。
机器学习与人工智能对高性能计算的需求推动了专用硬件加速器（如TPU、GPU、FPGA）的发展。通过降低算术运算精度可提升处理速度并降低延迟，这对实时AI应用至关重要。精度压缩能减少内存带宽需求和能耗，提升吞吐量，实现更多并行操作，最大化硬件利用率。该策略对常见于机器学习的NP-hard QUBO问题尤为重要，因其通常需高精度表示。量子退火等专用硬件求解器亦从中受益。本文提出一种基于动态范围作为复杂度度量的全理论分支定界算法，用于降低QUBO问题的精度需求。实验在实际量子退火机上验证了该算法的有效性。
原文摘要 · Abstract (English)
The demand for high-performance computing in machine learning and artificial intelligence has led to the development of specialized hardware accelerators like Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and Field-Programmable Gate Arrays (FPGAs). A key strategy to enhance these accelerators is the reduction of precision in arithmetic operations, which increases processing speed and lowers latency - crucial for real-time AI applications. Precision reduction minimizes memory bandwidth requirements and energy consumption, essential for large-scale and mobile deployments, and increases throughput by enabling more parallel operations per cycle, maximizing hardware resource utilization. This strategy is equally vital for solving NP-hard quadratic unconstrained binary optimization (QUBO) problems common in machine learning, which often require high precision for accurate representation. Special hardware solvers, such as quantum annealers, benefit significantly from precision reduction. This paper introduces a fully principled Branch-and-Bound algorithm for reducing precision needs in QUBO problems by utilizing dynamic range as a measure of complexity. Experiments validate our algorithm's effectiveness on an actual quantum annealer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。