论文标题
深度量子神经网络上的量子信息理论观点
A Quantum Information Theoretic View On A Deep Quantum Neural Network
论文作者
论文摘要
我们讨论了人工深神经网络的量子版本，在该网络中，神经元的作用被Qubits接管，而权重的作用是由单位人士发挥的。非线性激活函数的作用是通过随后追踪网络的图层（Qubits）来接管的。我们研究了两个示例，并从量子信息理论的角度讨论学习。详细说明，我们表明，海森堡不确定性关系的下限正在定义学习过程中梯度下降的变化。我们提出了一个问题，即在海森堡不确定性关系中量化的两个非公认可观察物的限制是否在裁定量子深神经网络的优化。我们找到一个负面答案。
We discuss a quantum version of an artificial deep neural network where the role of neurons is taken over by qubits and the role of weights is played by unitaries. The role of the non-linear activation function is taken over by subsequently tracing out layers (qubits) of the network. We study two examples and discuss the learning from a quantum information theoretic point of view. In detail, we show that the lower bound of the Heisenberg uncertainty relations is defining the change of the gradient descent in the learning process. We raise the question if the limit by Nature to two non-commuting observables, quantified in the Heisenberg uncertainty relations, is ruling the optimization of the quantum deep neural network. We find a negative answer.
