The Ising model, originally proposed a century ago, has become a cornerstone of combinatorial optimization in recent decades. However, Ising machines remain constrained by a fundamental hardware-speed trade-off. We introduce the Bounce-Bind Ising Machine (BBIM), a mechanism with a single tunable parameter that modulates spin dynamics without altering the energy landscape, building upon the classic golf-ball analogy but replacing it with a dynamic tennis ball/shot put system. The Bounce mode (accelerating escapes from local minima) and Bind mode (enabling rapid convergence) dynamically balance speed and quality. Benchmarked on dense MAX-CUT (edge density=0.5), BBIM achieves a peak speedup of 6.15 times at n=200. For sparse 3-Regular 3-XORSAT (second-order), the peak speedup reaches 27.3 times at n=160. Both results incur negligible additional hardware resource consumption. This work demonstrates a critical pathway to circumventing the hardware-speed bottleneck and its practical applicability to large-scale optimization hardware, validated on structurally distinct benchmarks.

翻译：伊辛模型虽于一个世纪前提出，但近几十年来已成为组合优化的基石。然而，伊辛机始终受限于硬件速度之间的根本性权衡。我们提出了弹跳-束缚伊辛机（BBIM），这是一种具有单一可调参数的机制，可在不改变能量景观的前提下调控自旋动力学；该机制基于经典的高尔夫球类比，但将其替换为动态的网球/铅球系统。弹跳模式（加速逃离局部极小值）与束缚模式（实现快速收敛）动态平衡了速度与求解质量。在稠密MAX-CUT（边密度=0.5）基准测试中，BBIM在n=200时实现了6.15倍的峰值加速比。对于稀疏的三正则三异或可满足性问题（二阶），在n=160时峰值加速比达到27.3倍。两项结果均仅带来可忽略的额外硬件资源消耗。本工作展示了一条突破硬件速度瓶颈的关键路径及其在大规模优化硬件中的实际适用性，并在结构迥异的基准测试中得到了验证。