张量网络收缩的高效并行化,用于模拟量子计算
Cupjin Huang1, Fang Zhang1,2, Michael Newman3
1Alibaba Quantum Laboratory, Alibaba Group USA, Bellevue, WA, USA.
Nature computational science
|January 13, 2024
概括
我们开发了一种用于收缩张量网络的新算法,使量子计算的高效经典模拟成为可能. 这种方法显著加速模拟,帮助量子算法和错误纠正开发.
科学领域:
- 量子计算是一种量子计算.
- 计算物理学的计算物理.
- 计算机科学 计算机科学
背景情况:
- 张量网络对于模拟复杂的量子系统至关重要.
- 大量的量子计算的经典模拟是计算密集的.
- 现有的方法面临着更大的量子系统的可扩展性挑战.
研究的目的:
- 开发一个高效的算法框架,用于收缩张量网络.
- 为了在前所未有的规模上实现量子计算的经典模拟.
- 加速量子算法和错误纠正技术的发展.
主要方法:
- 开发了一个用于张量网络收缩的算法框架.
- 引入了"索引切片",用于将收缩平行化成更小,独立的子任务.
- 在随机量子电路上对算法进行了基准测试.
主要成果:
- 与估计相比,在模拟成本方面实现了超过10^5倍的加速.
- 证明了框架模拟以前无法实现的量子计算的能力.
- 验证了框架在量子算法和错误纠正开发中的实用性.
结论:
- 开发的框架显著提高了量子系统的经典模拟能力.
- 索引切片为张量网络收缩提供了一个高效的并行化策略.
- 该框架在计算科学和量子技术开发中具有广泛的应用.
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