实用汉密尔顿式学习与单元动力学和吉布斯状态
Andi Gu1,2,3, Lukasz Cincio4, Patrick J Coles4,5
1Department of Physics, University of California, Berkeley, Berkeley, CA, USA. andigu@g.harvard.edu.
Nature communications
|January 8, 2024
概括
这项研究引入了一种改进的哈密尔顿学习协议,用于量子多体系统. 新方法增强了可扩展性,特别是对于系统结构参数,并为实际应用提供了最佳的超参数设置.
科学领域:
- 量子物理学 量子物理学 是一种量子物理学.
- 量子信息科学 量子信息科学
- 计算物理 计算物理
背景情况:
- 学习哈密尔顿参数对于理解量子多体系统至关重要.
- 现有的方法经常面临系统大小和复杂性的可扩展性挑战.
- 导数估计为哈密尔顿式学习提供了一个有希望的途径.
研究的目的:
- 为量子多体系统开发一个更可扩展的哈密尔顿学习协议.
- 为了改善对哈密尔顿结构参数的扩展依赖性,如局部.
- 为优化学习协议超参数提供精确的指南.
主要方法:
- 基于哈密尔顿式学习的导数估计技术.
- 提出了一种具有改进扩展特性的新型协议.
- 导出精确的性能极限以确定最佳的超参数设置.
- 使用80量子比特系统上的数值模拟来证明实用性.
主要成果:
- 拟议的协议显示了改进的缩放,特别是关于哈密尔顿定位的情况.
- 精确的边界为最佳的超参数调整 (例如,进化时间,温度) 提供了数值处方.
- 该方法显示了大型量子系统的实际可扩展性,通过模拟验证.
结论:
- 开发的哈密尔顿学习协议在可扩展性和精度方面提供了显著的优势.
- 这项工作为学习复杂量子系统参数提供了实际框架.
- 这些发现为更有效地表征大型量子系统铺平了道路.
相关概念视频
State Space Representation
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Transfer Function to State Space
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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
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State Space to Transfer Function
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Linear Approximation in Time Domain
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