相关实验视频
Updated: Jun 12, 2025

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Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
Published on: March 30, 2017
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从变量量子算法中的陷揭示量子相位过渡
Chenfeng Cao1,2, Filippo Maria Gambetta1, Ashley Montanaro1,3
1Phasecraft Ltd, London, United Kingdom.
概括
本研究引入了一种混合量子-经典算法来检测量子相位过渡. 该方法使用机器学习来识别关键点,提高低温物理系统的效率.
科学领域:
- 量子物理学的量子物理学
- 凝聚物质物理学 凝聚物质物理学
- 机器学习 机器学习
背景情况:
- 描述量子相变 (QPT) 对于理解低温物理系统至关重要.
- 确定基本状态和顺序参数是QPT研究中的关键挑战.
研究的目的:
- 开发一种混合量子-经典算法,以实现高效的QPT检测.
- 利用近期的量子计算机和机器学习来识别关键点.
主要方法:
- 一个混合算法,将量子优化和经典机器学习 (LASSO和变压器模型) 结合起来.
- 使用滑动窗口扫描哈密尔顿参数来学习顺序参数.
- 通过Rigetti的Ankaa 9Q-1量子计算机上的数值模拟和实验进行验证.
主要成果:
- 成功识别了常规和拓相位过渡.
- 证明有能力以提高效率和精度定位关键点.
- 在真实量子硬件上验证协议.
结论:
- 开发的混合协议为使用浅量子电路进行QPT调查提供了一个框架.
- 这种方法整合了近期量子计算和机器学习,用于凝聚物质研究.
- 该方法显示了提高QPT研究效率和精度的潜力.
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