Fock态神经网络的有效位编码
Efficient bit encoding of neural networks for Fock states
摘要：
我们提出了一个比特编码方案的玻色子福克数状态的限制玻尔兹曼机神经网络架构的高效和可扩展的表示。与常见的密度矩阵实现相比，神经网络的复杂度仅与比特编码神经元的数量而不是最大玻色子数成比例。至关重要的是，在高占领政权的信息压缩效率，甚至超过最大限度地优化的密度矩阵实现，其中投影仪的方法是用来访问稀疏的希尔伯特空间表示可用。
英文摘要：
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.
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