Fig. 4: A flow chart of the adaptive feedback-control algorithm using a Monte Carlo method.
From: Large-scale Ising emulation with four body interaction and all-to-all connections
Step 1: generate an initial random binary phase spin pattern on a spatial light modulator (SLM). Step 2: define the total energy of the system. Step 3: define the range for s and τ and run t random trials for each. Step 4: detect the pump and second harmonic (SH) powers. Step 5: find the energy difference U = Enew − Eold (where Eold is the previous minimum energy), Boltzmann's probability function \(P=\exp [-U/\tau ]\), and a random variable R ∈ [0, 1]. Step 6: check if the optimization criteria, U ≤ 0 or P > R, is met. Step 7: check the number of iterations. Step 8: if the criteria is not satisfied in Step 6 and Step 7, flip the spins within a randomly chosen cluster and update the binary phase mask on a SLM. Step 9: repeat Steps 3–8 if optimum criteria is not satisfied. Step 10: stop the feedback loop and collect the results.
