论文标题
量子深度学习中的全球最佳搜索
Global Optimum Search in Quantum Deep Learning
论文作者
论文摘要
本文旨在通过使用量子电路解决机器学习优化问题。提出了两种方法，分别是平均方法和部分掉期测试截止方法（PSTC）来搜索两个不同目标函数的全局最小值/最大值。当前的成本为$ O（\ sqrt {|θ|} n）$，但是有可能通过增强检查过程来进一步将PSTC改进到$ O（\ sqrt {| sqrt {|θ|} \ cdot sublinear \ n）$。
This paper aims to solve machine learning optimization problem by using quantum circuit. Two approaches, namely the average approach and the Partial Swap Test Cut-off method (PSTC) was proposed to search for the global minimum/maximum of two different objective functions. The current cost is $O(\sqrt{|Θ|} N)$, but there is potential to improve PSTC further to $O(\sqrt{|Θ|} \cdot sublinear \ N)$ by enhancing the checking process.
