有效实施[公式:参见文本]调整压缩传感与混乱振幅控制的连贯Ising机器
Mastiyage Don Sudeera Hasaranga Gunathilaka1, Satoshi Kako2, Yoshitaka Inui2
1School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa Japan.
Scientific reports
|September 26, 2023
概括
闭环连贯Ising机器 (CIM) 增强了量子-经典混合系统,用于解决复杂的优化问题. 这种改进的系统在压缩传感应用中表现出更高的准确性和有效性.
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.
- 信号处理 信号处理
背景情况:
- 一致的Ising机器 (CIM) 是设计用于通过找到Ising哈密尔顿的基本状态来解决组合优化问题的光学网络.
- 一个以前的量子-经典混合系统使用开放循环CIM进行基于[公式:参见文本]调整的压缩传感,缺乏振幅控制反.
- 这种限制可能会导致系统陷入局部最小值的困境,阻碍最佳解决方案的发现.
研究的目的:
- 通过实施封闭循环连贯定位机 (CIM) 来增强压缩传感的量子-经典混合系统.
- 调查闭环CIM是否通过在目标幅度周围实现混乱行为,可以改善在能源格局中逃离局部最小值.
- 用人造和磁共振图像数据来评估闭环系统与开环系统的性能.
主要方法:
- 在量子-古典混合系统中,为一致的Ising机器 (CIM) 实施一个闭环控制机制.
- 在闭环CIM中利用围绕目标振幅的混乱动态来促进逃离局部能量最小值.
- 使用人工和磁共振成像数据集对压缩传感问题的增强系统进行测试.
主要成果:
- 与开放循环系统相比,闭环CIM系统在解决优化问题方面表现出更好的准确性.
- 改进后的系统表现出更广泛的有效性,表明在不同的问题实例中表现得更好.
- 闭环控制引发的混乱行为似乎有助于更有效地导航能源格局.
结论:
- 闭环CIM的集成显著提高了压缩传感量子-经典混合系统的性能.
- 闭环方法提供了一种更强大的方法来逃避局部最小值,从而获得更准确和更有效的优化解决方案.
- 这项研究验证了闭环CIM在信号处理和优化的实际应用中的潜力.
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