QF-LCA数据集:量子场镜头编码算法用于系统状态模拟和强有力的预测
Philip Baback Alipour1, Thomas Aaron Gulliver1
1Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
Data in brief
|December 16, 2024
概括
量子场透镜编码算法 (QF-LCA) 数据集通过编码量子状态来模拟系统和预测事件. 这种量子人工智能 (QAI) 方法提高了状态过渡概率,提高了系统效率和预测准确度.
科学领域:
- 量子计算和人工智能的人工智能
- 热力学系统模拟系统
- 数据科学和预测建模预测模型
背景情况:
- 量子场透镜编码算法 (QF-LCA) 数据集在量子水平上编码系统状态.
- 现有的模拟热力学系统和预测事件的方法可以得到改进.
- 量子双场 (QDF) 计算模型为模拟复杂系统提供了一种新的方法.
研究的目的:
- 引入和验证QF-LCA数据集,用于模拟系统和预测事件.
- 展示QF-LCA和QDF模型如何增强状态过渡概率预测.
- 展示量子人工智能 (QAI) 的应用,以优化系统能量路径.
主要方法:
- 使用QF-LCA生成编码量子级系统状态的数据集.
- 使用量子双场 (QDF) 计算来模拟热力学系统和预测事件.
- 在生成的数据集上训练QAI分类器,以预测和重定向粒子能量路径,测量纠 (EE) 以进行状态分类.
主要成果:
- 当与QDF和QAI一起使用QF-LCA数据集时,会使状态转换概率翻一番,从而提高预测准确度.
- QAI分类器成功地预测和优化模拟N粒子系统中的能量路径,最大限度地提高效率.
- 纠 (EE) 测量有效地区分纠状态,有助于分类和系统状态预测.
结论:
- QF-LCA数据集为复杂系统中的QAI驱动的预测建模提供了坚实的基础.
- QDF计算和QAI集成使自动预测和分类成为可能,超越了手动分析.
- 应用范围跨越多个领域,包括数据科学,安全,法医学和粒子物理学,用于信息检索和系统优化.
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