论文标题
使用机器学习的猜测概率上的上限
Upper bound on the Guessing probability using Machine Learning
论文作者
论文摘要
猜测概率的估计在量子加密过程中至关重要。它也可以用作非局部相关性的见证人。在大多数研究的方案中，估计猜测概率等于解决了有效算法的半准程序。但是，这些程序的大小随系统的大小而成倍增长，即使对于少量的输入和输出也是不可行的。我们已经实施了一些相关的铃铛方案的深度学习方法，以面对这个问题。我们的结果表明，机器学习的能力是估计猜测概率和理解非局部性的能力。
The estimation of the guessing probability has paramount importance in quantum cryptographic processes. It can also be used as a witness for nonlocal correlations. In most of the studied scenarios, estimating the guessing probability amounts to solving a semi-definite programme, for which potent algorithms exist. However, the size of those programs grows exponentially with the system size, becoming infeasible even for small numbers of inputs and outputs. We have implemented deep learning approaches for some relevant Bell scenarios to confront this problem. Our results show the capabilities of machine learning for estimating the guessing probability and for understanding nonlocality.
