论文标题
量子光学神经网络中线性光学模块的有效训练性
Efficient trainability of linear optical modules in quantum optical neural networks
论文作者
论文摘要
对于通用离散可变量子神经网络而言，存在“贫瘠的高原景观”，该网络阻碍了全球测量所定义的基于梯度的优化成本函数的优化，对于量子光学神经网络中的通用线性光学模块而言，由于持续不断变化的持续变量较小的态度，在量子光学神经网络的通用线性光学模块中会令人惊讶。我们证明，如果$ M $模式中的连贯光通常可以有效地汇编，如果总强度尺度以$ M $为单位，并将此结果扩展到基于同性恋，杂尼或光子检测测量统计的成本函数，并将其扩展到弱点的成本功能。我们进一步证明了$ M $ MODE线性光学量子电路的有效训练性，用于用于变异的平均野外能量估计二次二次汉密尔顿人的投入状态，这些输入状态没有$ M $的能量指数呈指数呈指数化消失。
The existence of "barren plateau landscapes" for generic discrete variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in $m$ modes can be generically compiled efficiently if the total intensity scales sublinearly with $m$, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. We further demonstrate efficient trainability of $m$ mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with $m$.
