• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Quantum Chemistry >Machine learning and signed particles, an alternative and efficient way to simulate quantum systems
【24h】

Machine learning and signed particles, an alternative and efficient way to simulate quantum systems

机译：机器学习和签名粒子，替代和有效的模拟量子系统的方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently neural networks have been applied in the context of the signed particle formulation of quantum mechanics to rapidly and reliably compute the Wigner kernel of any provided potential. Important advantages were introduced, such as the reduction of the amount of memory required for the simulation of a quantum system by avoiding the storage of the kernel in a multi-dimensional array, as well as attainment of consistent speedup by the ability to realize the computation only on the cells occupied by signed particles. An inherent limitation was the number of hidden neurons to be equal to the number of cells of the discretized real space. In this work, anew network architecture is presented, decreasing the number of neurons in its hidden layer, thereby reducing the complexity of the network and achieving an additional speedup. The approach is validated on a onedimensional quantum system consisting of a Gaussian wave packet interacting with a potential barrier.
机译：最近，在量子力学的符号粒子制剂的上下文中已经应用了迅速且可靠地计算任何提供的潜力的Wigner核。 引入了重要的优点，例如通过避免在多维阵列中存储内核的存储来减少量子系统所需的内存量，以及通过实现计算的能力来实现一致的加速 仅在符号粒子占据的细胞上。 固有的限制是隐藏神经元的数量等于离散的真实空间的小区数量。 在这项工作中，呈现了重新的网络架构，减少了隐藏层中的神经元数，从而降低了网络的复杂性并实现了额外的加速。 该方法是在由潜在屏障相互作用的高斯波分组组成的一维尺度量子系统上验证。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号