【24h】

A Quantum Analog to Basis Function Networks

机译:量子模拟基函数网络

获取原文
获取原文并翻译 | 示例

摘要

A Fourier-based quantum computational learning algorithm with similarities to classical basis function networks is developed. Instead of a Gaussian basis, the quantum algorithm uses a discrete Fourier basis with the output being a linear combination of the basis. A set of examples is considered as a quantum system that undergoes unitary transformations to produce learning. The main result of the work is a quantum computational learning algorithm that is unique among quantum algorithms as it does not assume a priori knowledge of a function f.
机译:提出了一种与经典基函数网络相似的基于傅立叶的量子计算学习算法。代替高斯基础,量子算法使用离散傅立叶基础,输出是基础的线性组合。一组示例被认为是经过单一变换产生学习的量子系统。这项工作的主要结果是量子计算学习算法,该算法在量子算法中是独一无二的,因为它不假设函数f的先验知识。

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