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.

通用离散变量量子神经网络的“荒芜高原景观”的存在阻碍了全局测量定义的成本函数基于梯度的高效优化，这在量子光学神经网络中的通用线性光学模块的情况下将是令人惊讶的，这是由于连续变量状态的强度的可调性以及相关的group群的大小呈指数减小。我们证明，如果总强度与m呈亚线性比例关系，则m个模式下的相干光可以有效地得到一般编译。，然后将此结果扩展到基于零差，外差或光子检测测量统计数据的成本函数，并扩展到存在衰减的嘈杂成本函数。我们进一步证明了m模式线性光量子电路的有效可训练性，用于正二次哈密顿量的变分平均场能量估计，其输入状态的能量不随m呈指数消失。