量子增强的马尔科夫链蒙特卡洛
David Layden1, Guglielmo Mazzola2,3, Ryan V Mishmash4,5
1IBM Quantum, Almaden Research Center, San Jose, CA, USA. david.layden@ibm.com.
Nature
|July 12, 2023
概括
本研究介绍了马尔科夫链蒙特卡洛 (MCMC) 采样的量子算法. 它比经典方法更快地纠正分布,为机器学习和物理提供了潜在的加速.
科学领域:
- 量子计算
- 计算物理
- 机器学习
背景情况:
- 目前的量子处理器面临大小和错误率的限制.
- 短期量子算法通常集中在复杂的概率分布的采样上.
- 马尔科夫链蒙特卡洛 (MCMC) 是从分布中采样的一个关键技术.
研究的目的:
- 介绍并演示经典伊辛模型的波兹曼分布采样的量子算法.
- 解决目前量子硬件可以解决的有用采样问题的需求.
- 为MCMC提供一种可证明的量子方法.
主要方法:
- 开发了一种实现马尔科夫链蒙特卡洛 (MCMC) 的量子算法.
- 在当前量子硬件上实验证明了算法.
- 通过实验和经典模拟分析了融合率.
主要成果:
- 量子MCMC算法在较少的代过程中显示了趋同.
- 实验表明量子算法是强大的噪音.
- 模拟显示了古典MCMC方法的立方到四方多项式加速度.
结论:
- 开发的量子MCMC算法为解决有用的抽样问题提供了可行的途径.
- 经验加速表明有可能缓解机器学习,统计物理和优化中的计算瓶.
- 这项工作为量子计算机解决实际采样挑战打开了道路.
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