From d158d022dc4bb84025b9b31cde3eeb0e57561023 Mon Sep 17 00:00:00 2001
From: Nuwan Yapa <yapanuwan@gmail.com>
Date: Wed, 4 Jun 2025 16:01:43 -0400
Subject: [PATCH] Delete Julia implementation of PMM

---
 calculations/PMM.jl | 71 ---------------------------------------------
 1 file changed, 71 deletions(-)
 delete mode 100644 calculations/PMM.jl

diff --git a/calculations/PMM.jl b/calculations/PMM.jl
deleted file mode 100644
index f408738..0000000
--- a/calculations/PMM.jl
+++ /dev/null
@@ -1,71 +0,0 @@
-using LinearAlgebra, Random, Plots
-include("../p_space.jl")
-
-μ = 0.5
-V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
-
-training_c = range(1.2, 0.9, 9) # original: range(1.35, 0.9, 5)
-extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
-
-# calculate training data
-data_c = training_c
-data_E = [quick_pole_E(V_system(c)) for c in data_c]
-
-# hyperparameters
-N = 9
-
-# initialize random Hamiltonians
-H0 = randn(ComplexF64, N, N)
-H0 = H0 + transpose(H0) # symmetric
-H1 = randn(ComplexF64, N, N)
-H1 = H1 + transpose(H1) # symmetric
-
-# training
-Es = ComplexF64[]
-ψs = Vector{ComplexF64}[]
-
-lr = 0.05
-epochs = 100000
-for epoch in 1:epochs
-    empty!(Es)
-    empty!(ψs)
-    for (c, E) in zip(data_c, data_E)
-        H = H0 + c * H1
-        evals, evecs = eigen(H)
-        i = nearestIndex(evals, E) # TODO: more robust way to identify the eigenvector
-        push!(Es, evals[i])
-        push!(ψs, evecs[:, i])
-    end
-
-    if epoch % 1000 == 0
-        loss = sum(abs2, Es .- data_E)
-        println("Epoch:$epoch/$epochs \t Loss: $loss")
-    end
-
-    # gradient of the loss function
-    function grad(c_order=0)
-        out = zeros(ComplexF64, N, N)
-        for (c, E_target, ψ, E) in zip(data_c, data_E, ψs, Es)
-            out .+= (c^c_order * conj(E - E_target)) .* (ψ * transpose(ψ))
-        end
-        return 2 .* real.(out)
-    end
-    H0 .-= lr .* grad(0) # update H0
-    H1 .-= lr .* grad(1) # update H1    
-end
-
-# evaluate for all points
-all_c = vcat(training_c, extrapolating_c)
-exact_E = [quick_pole_E(V_system(c)) for c in all_c]
-extrapolated_E = ComplexF64[]
-for (c, ref) in zip(all_c, exact_E)
-    H = H0 + c * H1
-    evals, evecs = eigen(H)
-    evals = vcat(evals, conj.(evals)) # include complex conjugates
-    push!(extrapolated_E, nearest(evals, ref))
-end
-
-# plot results
-scatter(real.(data_E), imag.(data_E), label="training", title="PMM", xlabel="Re E", ylabel="Im E")
-scatter!(real.(exact_E), imag.(exact_E), label="exact", m=:+)
-scatter!(real.(extrapolated_E), imag.(extrapolated_E), label="predicted", m=:x)