AI 中文总结
该研究提出跳扩散随机量化方法，利用跳扩散过程的非连续路径特性改善遍历性，解决二维U(1)规范理论拓扑冻结的基准问题。
AI 中文摘要
我们通过考虑跳扩散过程，构造了马尔可夫意义下随机量化的自然推广。这类随机过程具有非连续路径，即所谓的莱维飞行。在存在跳跃时，可高效探索带有势垒的作用量景观，在传统扩散方法失效的区域改善甚至恢复遍历性。我们探索了多种构建高效跳跃更新的策略，并将其用于解决二维U(1)规范理论中拓扑冻结的基准问题。
英文摘要
We construct the natural generalization of stochastic quantization (in the Markovian sense) by considering jump-diffusion processes. This class of stochastic processes exhibits non-continuous paths, so-called Lévy flights. In the presence of jumps, action landscapes with barriers can be efficiently explored, improving and even restoring ergodicity where traditional diffusion approaches become inefficient. We explore different strategies for constructing efficient jump updates, which we deploy to address the benchmark problem of topological freezing in 2d U(1) gauge theory.
Comments15 pages, 4 figures
