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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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QRBT:用于可扩展区块链交易处理的量子驱动增强学习.

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  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

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此摘要是机器生成的。

量子驱动增强学习 (QRBT) 通过将延迟时间降低高达91%,并提高对量子攻击的安全性来增强区块链交易处理. 这种新的框架为未来的区块链系统提供了可扩展和节能的解决方案.

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科学领域:

  • 量子计算是一种量子计算.
  • 人工智能的人工智能
  • 区块链技术 区块链技术

背景情况:

  • 区块链交易处理在可扩展性,延迟性和量子安全性方面面临重大挑战.
  • 现有的解决方案难以平衡高吞吐量与强大的加密弹性,以应对新出现的量子威胁.

研究的目的:

  • 引入QRBT (量子驱动增强学习),这是一个旨在克服区块链扩展和延迟问题的框架,同时确保量子安全.
  • 利用量子计算和强化学习来改善共识和交易验证.

主要方法:

  • QRBT采用了四层架构:量子计算,强化学习,区块链安全和交易处理.
  • 它将变量量子电路和量子密钥分布 (QKD) 与一个演员关键强化学习范式集成在一起.
  • 量子增强状态编码和电路改进驱动自适应性政策优化.

主要成果:

  • 交易延迟减少了高达91.264%,加密安全性提高了高达96.152%,吞吐量提高了92.635%.
  • 共识能源消耗被最小化,强化学习趋同有效地稳定,优于基线方法.
  • QRBT证明了对量子攻击的同时高吞吐量,安全性和能源效率.

结论:

  • 量子辅助增强学习为下一代区块链系统提供了可扩展和安全的方法.
  • QRBT有效地解决了区块链性能和量子对手抵抗的关键挑战.