发表机构
Yale University; University of Toronto; Pacific Northwest National Laboratory（耶鲁大学; 多伦多大学; 太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出并行化耗散量子算法中跳跃算子的方案，利用几何局域性使电路深度指数级降低，实验显示100量子比特一维系统查询深度减少53倍，提升了近期量子计算的实用性。
AI 中文摘要
受经典马尔可夫链蒙特卡洛（MCMC）方法的启发，耗散量子算法是一类新兴的在量子计算机上制备量子态的方法。它们通过模拟由称为林德布拉德算子的数学算子所控制的耗散过程来运作。这些算法的性能可能受到林德布拉德算子组成跳跃算子顺序实现的限制，这导致电路深度较高。为解决这一问题，本工作分析了一种并行化方案以同时实现跳跃算子。我们利用几何局域性的结构来表明单个跳跃算子保持空间局域性。这使每个时间步的电路深度指数级减少，从而带来整体多项式改进。数值实验表明，对于100量子比特的一维基准系统，查询深度减少了53倍。电路深度的降低使耗散算法在近期量子计算中更加实用。
英文摘要
Inspired by classical Markov Chain Monte Carlo (MCMC) methods, dissipative quantum algorithms are an emerging class of methods for preparing quantum states on a quantum computer. They operate by simulating a dissipative process governed by a mathematical operator known as the Lindbladian. The performance of these algorithms can be limited by the sequential implementation of the Lindbladian's constituent jump operators, which leads to high circuit depth. To address this issue, this work analyzes a parallelization scheme to implement jump operators simultaneously. We leverage the structure of geometric locality to show that individual jump operators remain spatially localized. This exponentially reduces the circuit depth per time step, resulting in an overall polynomial improvement. Numerical experiments demonstrate a 53x reduction in query depth for a 1D benchmark system of 100 qubits. The reduced circuit depth makes dissipative algorithms more practical for near-term quantum computing.
