Figure 2
From: Efficient excitation-transfer across fully connected networks via local-energy optimization
Example of learning curve using the approach presented in Sect. 3. The final sink population \(r_{s}^{T}\) is optimized with respect to h⃗, and we plot it as a function of the number of iterations of RMSprop, with \(T=5\). The numerical values of the \(H_{I}\) entries are those given by Equation (16), the decaying rates are given by \(\Gamma _{n}=\Gamma =5\cdot 10^{-4}\), \(\Gamma _{s}=6.283\), while we assume that \(\gamma _{n}=0\) for any value of n, meaning that the network is not subject to any dephasing. We assume that the excitation is initially injected in the first site of the network, i.e., \(\rho (0)=|1\rangle \langle 1|\)