We present a bit encoding scheme for a highly efficient and scalable representation of bosonic Fock number states in the restricted Boltzmann machine neural network architecture. In contrast to common density matrix implementations, the complexity of the neural network scales only with the number of bit-encoded neurons rather than the maximum boson number. Crucially, in the high occupation regime its information compression efficiency is shown to surpass even maximally optimized density matrix implementations, where a projector method is used to access the sparsest Hilbert space representation available.

中文翻译：

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用于 Fock 状态的神经网络的有效位编码

我们提出了一种比特编码方案，用于在受限玻尔兹曼机器神经网络架构中高效且可扩展地表示玻色 Fock 数状态。与常见的密度矩阵实现相反，神经网络的复杂性仅与位编码神经元的数量有关，而不是与最大玻色子数有关。至关重要的是，在高占用状态下，其信息压缩效率甚至超过了最大优化的密度矩阵实现，其中使用投影仪方法访问可用的最稀疏希尔伯特空间表示。