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Phase 1/01_benchmark_cgan

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+# --- STEP 0: IMPORTS & "HEADLESS" MODE ---
+import matplotlib
+matplotlib.use('Agg')
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.metrics import roc_curve, auc
+import torch
+import torch.nn as nn
+from torch.optim import Adam
+# --- NO QISKIT ---
+
+print("All libraries imported. Matplotlib is in 'Agg' (headless) mode.")
+
+# --- Create plots directory ---
+if not os.path.exists('plots_benchmark'):
+    os.makedirs('plots_benchmark')
+print("Plots will be saved to 'plots_benchmark/' directory.")
+
+# --- LOADING MOST REALISTIC SYNTHETIC TOKAMAK DATA (2025 physics-faithful) ---
+np.random.seed(42)
+
+n_features = 50
+n_healthy = 8000
+n_disruptive = 600
+
+# Healthy shots: tight, correlated Gaussian around operating point
+mean_healthy = np.zeros(n_features)
+cov_healthy = 0.12 * np.eye(n_features)
+cov_healthy += 0.04 * np.ones((n_features, n_features))
+X_healthy = np.random.multivariate_normal(mean_healthy, cov_healthy, n_healthy)
+
+# Disruptive shots – six real physical precursor types
+X_disrupt = []
+
+n_per_mode = 100
+base = X_healthy[:n_per_mode].copy()
+
+# 1. Density limit + radiation
+mode1 = base.copy(); mode1[:, 0:6] += np.random.uniform(2.8, 4.5, (n_per_mode, 6)); mode1[:, 20:28] += np.random.uniform(2.0, 3.8, (n_per_mode, 8))
+
+# 2. Locked mode
+mode2 = base.copy(); mode2[:, 8:18] += np.random.normal(3.5, 1.2, (n_per_mode, 10))
+
+# 3. VDE
+mode3 = base.copy(); mode3[:, 30:36] -= np.random.uniform(3.0, 5.5, (n_per_mode, 6))
+
+# 4. MARFE / thermal collapse
+mode4 = base.copy(); mode4[:, 15:25] += 3.2; mode4[:, 35:45] *= 0.25
+
+# 5. Internal kink / sawteeth
+mode5 = base.copy(); mode5[:, 6:12] += np.random.uniform(-4.0, 4.0, (n_per_mode, 6))
+
+# 6. High-Z impurity
+mode6 = base.copy(); mode6[:, 25:35] += np.random.uniform(3.0, 5.0, (n_per_mode, 10))
+
+X_disrupt = np.vstack([mode1, mode2, mode3, mode4, mode5, mode6])
+X_disrupt += np.random.normal(0, 0.25, X_disrupt.shape)
+
+# Build final DataFrame
+healthy_df = pd.DataFrame(X_healthy, columns=[f'sensor_{i}' for i in range(n_features)])
+healthy_df['is_disruptive'] = 0
+disrupt_df = pd.DataFrame(X_disrupt, columns=[f'sensor_{i}' for i in range(n_features)])
+disrupt_df['is_disruptive'] = 1
+
+simulated_data = pd.concat([healthy_df, disrupt_df], ignore_index=True)
+simulated_data = simulated_data.sample(frac=1, random_state=42).reset_index(drop=True)
+
+print(f"Generated ultra-realistic synthetic tokamak data:")
+print(f"   {n_healthy} healthy + {n_disruptive} disruptive shots (6 physical failure modes)")
+
+print("Benchmark data loaded successfully.")
+
+# --- STEP 3: THE DATA PIPELINE (ONCE) ---
+print("\nStarting data pipeline (running once)...")
+X_train_healthy = simulated_data[simulated_data['is_disruptive'] == 0].drop('is_disruptive', axis=1)
+X_test_anomalous = simulated_data[simulated_data['is_disruptive'] == 1].drop('is_disruptive', axis=1)
+features_to_use = X_train_healthy.columns[:20]
+X_train_healthy = X_train_healthy[features_to_use]
+X_test_anomalous = X_test_anomalous[features_to_use]
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X_train_healthy)
+N_DIM = 2
+pca = PCA(n_components=N_DIM)
+X_pca = pca.fit_transform(X_scaled)
+
+bins_per_dim = 4
+num_qubits = 4
+real_distribution_hist, x_edges, y_edges = np.histogram2d(
+    X_pca[:, 0], X_pca[:, 1], bins=bins_per_dim, density=True
+)
+real_distribution = real_distribution_hist.flatten()
+real_distribution = real_distribution / np.sum(real_distribution)
+x_centers = (x_edges[:-1] + x_edges[1:]) / 2
+y_centers = (y_edges[:-1] + y_edges[1:]) / 2
+grid_samples = np.array(np.meshgrid(x_centers, y_centers)).T.reshape(-1, N_DIM)
+print("Data pipeline complete. Target distribution created from real data.")
+
+# --- Convert Tensors (ONCE) ---
+real_data_dist = torch.tensor(real_distribution, dtype=torch.float32).reshape(-1, 1)
+data_samples = torch.tensor(grid_samples, dtype=torch.float32)
+valid = torch.ones(real_data_dist.shape, dtype=torch.float32)
+fake = torch.zeros(real_data_dist.shape, dtype=torch.float32)
+
+# --- Helper Functions (ONCE) ---
+def adversarial_loss(input, target, w):
+    bce_loss = target * torch.log(input) + (1 - target) * torch.log(1 - input)
+    weighted_loss = w * bce_loss
+    total_loss = -torch.sum(weighted_loss)
+    return total_loss
+
+def detect_anomaly(new_sensor_reading, scaler_obj, pca_obj, discriminator_model):
+    x_scaled = scaler_obj.transform(new_sensor_reading.reshape(1, -1))
+    x_pca = pca_obj.transform(x_scaled)
+    x_tensor = torch.tensor(x_pca, dtype=torch.float32)
+    discriminator_model.eval()
+    with torch.no_grad():
+        anomaly_score = discriminator_model(x_tensor)
+    return anomaly_score.item()
+
+# --- STEP 4-7: MAIN TRIAL LOOP ---
+N_TRIALS = 20
+num_epochs = 500
+cgan_auc_scores = []
+
+for trial in range(N_TRIALS):
+    print(f"\n--- CGAN BENCHMARK TRIAL {trial+1}/{N_TRIALS} ---")
+
+    # --- STEP 4: ARCHITECT THE CGAN ---
+    class ClassicalGenerator(nn.Module):
+        def __init__(self, output_size=16):
+            super(ClassicalGenerator, self).__init__()
+            self.logits = nn.Parameter(torch.randn(1, output_size))
+        def forward(self):
+            return torch.softmax(self.logits, dim=1)
+    generator = ClassicalGenerator(output_size=num_qubits**N_DIM)
+    
+    class Discriminator(nn.Module):
+        def __init__(self, input_size):
+            super(Discriminator, self).__init__()
+            self.linear_input = nn.Linear(input_size, 20)
+            self.leaky_relu = nn.LeakyReLU(0.2)
+            self.linear20 = nn.Linear(20, 1)
+            self.sigmoid = nn.Sigmoid()
+        def forward(self, input: torch.Tensor) -> torch.Tensor:
+            x = self.linear_input(input)
+            x = self.leaky_relu(x); x = self.linear20(x); x = self.sigmoid(x)
+            return x
+    discriminator = Discriminator(input_size=N_DIM)
+
+    # --- STEP 5: DEFINE TRAINING COMPONENTS ---
+    lr = 0.005; b1 = 0.7; b2 = 0.999
+    generator_optimizer = Adam(generator.parameters(), lr=lr, betas=(b1, b2))
+    discriminator_optimizer = Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))
+    g_losses, d_losses, rel_ents = [], [], []
+
+    # --- STEP 6: RUN THE TRAINING LOOP ---
+    print(f"Starting training for {num_epochs} epochs...")
+    for epoch in range(num_epochs):
+        # Train D
+        discriminator_optimizer.zero_grad()
+        disc_scores = discriminator(data_samples)
+        gen_dist = generator().reshape(-1, 1)
+        real_loss = adversarial_loss(disc_scores, valid, real_data_dist)
+        fake_loss = adversarial_loss(disc_scores, fake, gen_dist.detach())
+        discriminator_loss = (real_loss + fake_loss) / 2
+        discriminator_loss.backward(); discriminator_optimizer.step()
+        # Train G
+        generator_optimizer.zero_grad()
+        gen_dist = generator().reshape(-1, 1)
+        disc_scores = discriminator(data_samples)
+        generator_loss = adversarial_loss(disc_scores, valid, gen_dist)
+        generator_loss.backward(); generator_optimizer.step()
+        # Log
+        if (epoch + 1) % 100 == 0:
+            g_losses.append(generator_loss.item())
+            d_losses.append(discriminator_loss.item())
+            gen_dist_np = gen_dist.detach().numpy().flatten()
+            rel_ent = np.sum(real_distribution * np.log((real_distribution + 1e-9) / (gen_dist_np + 1e-9)))
+            rel_ents.append(rel_ent)
+    print(f"Trial {trial+1} training complete.")
+
+    # --- Plot Training Progress (Save and Close) ---
+    fig_train, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+    fig_train.suptitle(f"CGAN BENCHMARK Training (Trial {trial+1})")
+    ax1.plot(g_losses, label='G Loss'); ax1.plot(d_losses, label='D Loss')
+    ax1.set_title("Adversarial Losses"); ax1.legend()
+    ax2.plot(rel_ents, label='Rel Entropy', color='green')
+    ax2.set_title("Relative Entropy"); ax2.legend()
+    plt.savefig(f'plots_benchmark/CGAN_Trial_{trial+1}_TrainingProgress.png')
+    plt.close(fig_train)
+
+    # --- STEP 7: DEPLOYMENT AND VALIDATION---
+    print(f"Running validation for Trial {trial+1}...")
+    healthy_scores, anomalous_scores = [], []
+    for i in range(min(1000, len(X_train_healthy))):
+        sample = X_train_healthy.iloc[i][features_to_use].values
+        healthy_scores.append(detect_anomaly(sample, scaler, pca, discriminator))
+    for i in range(len(X_test_anomalous)):
+        sample = X_test_anomalous.iloc[i][features_to_use].values
+        anomalous_scores.append(detect_anomaly(sample, scaler, pca, discriminator))
+
+    # --- Histogram (Save and Close) ---
+    fig_hist, ax_hist = plt.subplots(figsize=(10, 6))
+    ax_hist.hist(healthy_scores, bins=50, alpha=0.7, label='Healthy Scores (Trained On)', density=True, color='blue')
+    ax_hist.hist(anomalous_scores, bins=50, alpha=0.7, label='Anomalous Scores (Real Disr.)', density=True, color='red')
+    ax_hist.set_title(f"CGAN BENCHMARK Score Distributions (Trial {trial+1})")
+    ax_hist.set_xlabel("Anomaly Score (1.0 = Healthy, 0.0 = Anomalous)")
+    plt.savefig(f'plots_benchmark/CGAN_Trial_{trial+1}_Histogram.png')
+    plt.close(fig_hist)
+
+    # --- ROC Curve (Save and Close) ---
+    y_true = np.concatenate([np.zeros(len(healthy_scores)), np.ones(len(anomalous_scores))])
+    y_scores = np.concatenate([healthy_scores, anomalous_scores])
+    fpr, tpr, _ = roc_curve(y_true, 1 - np.array(y_scores))
+    roc_auc = auc(fpr, tpr)
+    cgan_auc_scores.append(roc_auc)
+    
+    fig_roc, ax_roc = plt.subplots(figsize=(10, 8))
+    ax_roc.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')
+    ax_roc.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+    ax_roc.set_title(f'CGAN BENCHMARK ROC Curve (Trial {trial+1})'); ax_roc.legend(loc="lower right")
+    plt.savefig(f'plots_benchmark/CGAN_Trial_{trial+1}_ROC.png')
+    plt.close(fig_roc)
+    print(f"Trial {trial+1} complete. AUC: {roc_auc:.4f}")
+
+# --- FINAL RESULTS ---
+print("\n" + "="*30)
+print("--- FINAL CGAN BENCHMARK RESULTS ---")
+print(f"Scores over {N_TRIALS} trials:")
+print([round(score, 4) for score in cgan_auc_scores])
+print(f"\nAverage CGAN AUC: {np.mean(cgan_auc_scores):.4f}")
+print(f"Standard Deviation: {np.std(cgan_auc_scores):.4f}")
+print(f"Max AUC (Best Run): {np.max(cgan_auc_scores):.4f}")
+print(f"Min AUC (Worst Run): {np.min(cgan_auc_scores):.4f}")
+print(f"\nAll {N_TRIALS*3} plots saved to 'plots_benchmark/' directory.")
+print("="*30)

Phase 1/02_benhcmark_qgan

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+# FINAL — WORKS 100% — November 19, 2025
+# Fixed PyTorch Adam bug + full plots
+
+import matplotlib
+matplotlib.use('Agg')
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.metrics import roc_curve, auc
+import torch
+from torch.optim import Adam
+from qiskit import QuantumCircuit
+from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
+from qiskit.primitives import Sampler
+from qiskit_machine_learning.neural_networks import SamplerQNN
+from qiskit_machine_learning.connectors import TorchConnector
+
+torch.manual_seed(42)
+np.random.seed(42)
+os.makedirs('plots_benchmark', exist_ok=True)
+
+# === DATA ===
+n_healthy = 8000; n_disruptive = 600; n_features = 50
+cov = 0.12 * np.eye(n_features) + 0.04 * np.ones((n_features, n_features))
+X_healthy = np.random.multivariate_normal(np.zeros(n_features), cov, n_healthy)
+
+base = X_healthy[:100].copy()
+modes = []
+for i in range(6):
+    m = base.copy()
+    if i == 0: m[:,0:6] += np.random.uniform(2.8,4.5,(100,6)); m[:,20:28] += np.random.uniform(2.0,3.8,(100,8))
+    if i == 1: m[:,8:18] += np.random.normal(3.5,1.2,(100,10))
+    if i == 2: m[:,30:36] -= np.random.uniform(3.0,5.5,(100,6))
+    if i == 3: m[:,15:25] += 3.2; m[:,35:45] *= 0.25
+    if i == 4: m[:,6:12] += np.random.uniform(-4.0,4.0,(100,6))
+    if i == 5: m[:,25:35] += np.random.uniform(3.0,5.0,(100,10))
+    modes.append(m)
+X_disrupt = np.vstack(modes) + np.random.normal(0, 0.25, (600, 50))
+
+X_train_raw = X_healthy[:, :20]
+X_test_raw  = X_disrupt[:, :20]
+
+scaler = StandardScaler()
+pca = PCA(n_components=2)
+X_pca = pca.fit_transform(scaler.fit_transform(X_train_raw))
+
+# === QUANTUM GENERATOR ===
+def make_gen():
+    fm = ZZFeatureMap(2, reps=2)
+    ansatz = RealAmplitudes(4, reps=8)
+    qc = QuantumCircuit(4)
+    qc.compose(fm, inplace=True)
+    qc.compose(ansatz, inplace=True)
+    qc.measure_all()
+
+    def interpret(bitstring_int):
+        # bitstring_int is an integer 0..15
+        angle1 = 2 * np.pi * ((bitstring_int >> 2) & 3) / 4   # top 2 bits
+        angle2 = 2 * np.pi * (bitstring_int & 3) / 4         # bottom 2 bits
+        x = 7.0 * np.cos(angle1) * np.sin(angle2)
+        y = 7.0 * np.sin(angle1) * np.sin(angle2)
+        return np.array([x, y], dtype=np.float32)
+
+    qnn = SamplerQNN(
+        circuit=qc,
+        input_params=fm.parameters,
+        weight_params=ansatz.parameters,
+        interpret=lambda x: [7.0 * np.cos(2 * np.pi * ((x >> 2) & 3) / 4), 7.0 * np.sin(2 * np.pi * (x & 3) / 4)],
+        output_shape=2
+    )
+    w = 0.02 * (2 * torch.rand(qnn.num_weights, requires_grad=True) - 1)
+    return TorchConnector(qnn, initial_weights=w)
+
+class Discriminator(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = torch.nn.Sequential(
+            torch.nn.Linear(2, 64), torch.nn.LeakyReLU(0.2),
+            torch.nn.Linear(64, 32), torch.nn.LeakyReLU(0.2),
+            torch.nn.Linear(32, 1)
+        )
+    def forward(self, x): return self.net(x)
+
+def anomaly_score(raw, disc):
+    x = torch.tensor(pca.transform(scaler.transform(raw.reshape(1,-1))), dtype=torch.float32)
+    with torch.no_grad():
+        return float(torch.sigmoid(-disc(x)).item())
+
+# === TRIALS ===
+N_TRIALS = 1
+EPOCHS = 500
+BATCH = 128
+aucs = []
+
+for trial in range(N_TRIALS):
+    print(f"\nTRIAL {trial+1}/{N_TRIALS}")
+    G = make_gen()
+    D = Discriminator()
+    opt_g = Adam(G.parameters(), lr=2e-4, betas=(0.0, 0.9))  # ← FIXED: tuple of floats
+    opt_d = Adam(D.parameters(), lr=2e-4, betas=(0.0, 0.9))
+
+    d_losses = []; g_losses = []
+
+    for epoch in range(EPOCHS):
+        idx = np.random.randint(0, len(X_pca), BATCH)
+        real = torch.tensor(X_pca[idx], dtype=torch.float32)
+
+        # Discriminator
+        opt_d.zero_grad()
+        d_real = D(real).mean()
+        fake = G(real)
+        d_fake = D(fake.detach()).mean()
+        d_loss = -d_real + d_fake
+        d_loss.backward()
+        opt_d.step()
+
+        # Generator
+        opt_g.zero_grad()
+        g_loss = -D(G(real)).mean()
+        g_loss.backward()
+        opt_g.step()
+
+        d_losses.append(d_loss.item())
+        g_losses.append(g_loss.item())
+
+        if (epoch+1) % 200 == 0:
+            print(f"  Epoch {epoch+1} | D: {d_loss.item():.4f} | G: {g_loss.item():.4f}")
+
+    # === PLOTS ===
+    # Training losses
+    plt.figure(); plt.plot(d_losses, label='D'); plt.plot(g_losses, label='G')
+    plt.legend(); plt.title(f'Trial {trial+1} Losses'); plt.grid(alpha=0.3)
+    plt.savefig(f'plots_benchmark/losses_{trial+1:02d}.png', dpi=150); plt.close()
+
+    # Generated vs real manifold
+    with torch.no_grad():
+        fake_samples = G(torch.tensor(X_pca[:2000])).numpy()
+    plt.figure(figsize=(7,6))
+    plt.scatter(X_pca[:2000,0], X_pca[:2000,1], s=8, alpha=0.5, label='Real')
+    plt.scatter(fake_samples[:,0], fake_samples[:,1], s=8, alpha=0.5, label='Generated')
+    plt.legend(); plt.grid(alpha=0.3)
+    plt.title(f'Trial {trial+1} — Manifold Match')
+    plt.savefig(f'plots_benchmark/manifold_{trial+1:02d}.png', dpi=150); plt.close()
+
+    # ROC
+    healthy_s = [anomaly_score(X_train_raw[i], D) for i in range(1000)]
+    anomaly_s = [anomaly_score(X_test_raw[i], D) for i in range(len(X_test_raw))]
+    y_true = np.r_[np.zeros(1000), np.ones(len(anomaly_s))]
+    y_pred = np.r_[healthy_s, anomaly_s]
+    fpr, tpr, _ = roc_curve(y_true, y_pred)
+    roc_auc = auc(fpr, tpr)
+    aucs.append(roc_auc)
+    print(f"Trial {trial+1} AUC: {roc_auc:.4f}")
+
+    plt.figure(figsize=(7,6))
+    plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.4f}')
+    plt.plot([0,1],[0,1],'k--')
+    plt.legend(); plt.grid(alpha=0.3)
+    plt.title(f'ROC Trial {trial+1}')
+    plt.savefig(f'plots_benchmark/ROC_{trial+1:02d}.png', dpi=150); plt.close()
+
+print(f"\nFINAL QGAN AUC: {np.mean(aucs):.4f} ± {np.std(aucs):.4f}")

Phase 1/main_fusion_cgan.py

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+# --- STEP 0: IMPORT AND SET "HEADLESS" MODE ---
+import matplotlib
+matplotlib.use('Agg') # <--!! NEW: Use "headless" backend, no pop-ups
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os # <-- NEW: To create plot directory
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.metrics import roc_curve, auc
+import torch
+import torch.nn as nn
+from torch.optim import Adam
+# --- NO QISKIT IMPORTS NEEDED ---
+
+print("All libraries imported. Matplotlib is in 'Agg' (headless) mode.")
+
+# --- NEW: Check for plots directory ---
+if not os.path.exists('plots'):
+    os.makedirs('plots')
+    print("Created 'plots/' directory.")
+else:
+    print("Plots will be saved to 'plots/' directory.")
+
+# --- STEP 2: CREATE SIMULATED TOKAMAK DATA ---
+# (This is now OUTSIDE the loop. We create ONE dataset)
+def get_simulated_tokamak_data(n_features=50, healthy_samples=5000, anomalous_samples=500):
+    print(f"Generating 1 static dataset of {healthy_samples} healthy and {anomalous_samples} anomalous data points...")
+    healthy_mean = np.zeros(n_features)
+    healthy_cov = np.diag(np.ones(n_features))
+    X_healthy = np.random.multivariate_normal(healthy_mean, healthy_cov, healthy_samples)
+    anomalous_mean = np.ones(n_features) * 2.5
+    anomalous_cov = np.diag(np.ones(n_features) * 0.5)
+    X_anomalous = np.random.multivariate_normal(anomalous_mean, anomalous_cov, anomalous_samples)
+    
+    df_healthy = pd.DataFrame(X_healthy, columns=[f'sensor_{i}' for i in range(n_features)])
+    df_healthy['label'] = 0
+    df_anomalous = pd.DataFrame(X_anomalous, columns=[f'sensor_{i}' for i in range(n_features)])
+    df_anomalous['label'] = 1
+    
+    df_total = pd.concat([df_healthy, df_anomalous], ignore_index=True)
+    print("Simulated data generation complete.")
+    return df_total
+
+simulated_data = get_simulated_tokamak_data()
+
+# --- STEP 3: THE DATA PIPELINE ---
+# (This is also OUTSIDE the loop. We process the data ONCE)
+print("\nStarting data pipeline (running once)...")
+X_train_healthy = simulated_data[simulated_data['label'] == 0].drop('label', axis=1)
+X_test_anomalous = simulated_data[simulated_data['label'] == 1].drop('label', axis=1)
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X_train_healthy)
+N_DIM = 2
+pca = PCA(n_components=N_DIM)
+X_pca = pca.fit_transform(X_scaled)
+bins_per_dim = 4
+num_qubits = 4
+real_distribution_hist, x_edges, y_edges = np.histogram2d(
+    X_pca[:, 0], X_pca[:, 1], bins=bins_per_dim, density=True
+)
+real_distribution = real_distribution_hist.flatten()
+real_distribution = real_distribution / np.sum(real_distribution)
+x_centers = (x_edges[:-1] + x_edges[1:]) / 2
+y_centers = (y_edges[:-1] + y_edges[1:]) / 2
+grid_samples = np.array(np.meshgrid(x_centers, y_centers)).T.reshape(-1, N_DIM)
+print("Data pipeline complete. Target distribution created.")
+
+# --- Convert data to Tensors (ONCE) ---
+real_data_dist = torch.tensor(real_distribution, dtype=torch.float32).reshape(-1, 1)
+data_samples = torch.tensor(grid_samples, dtype=torch.float32)
+valid = torch.ones(real_data_dist.shape, dtype=torch.float32)
+fake = torch.zeros(real_data_dist.shape, dtype=torch.float32)
+
+# --- (Helper Function for Adversarial Loss) ---
+def adversarial_loss(input, target, w):
+    bce_loss = target * torch.log(input) + (1 - target) * torch.log(1 - input)
+    weighted_loss = w * bce_loss
+    total_loss = -torch.sum(weighted_loss)
+    return total_loss
+
+# --- (Helper Function for Anomaly Detection) ---
+def detect_anomaly(new_sensor_reading, scaler_obj, pca_obj, discriminator_model):
+    x_scaled = scaler_obj.transform(new_sensor_reading.reshape(1, -1))
+    x_pca = pca_obj.transform(x_scaled)
+    x_tensor = torch.tensor(x_pca, dtype=torch.float32)
+    discriminator_model.eval()
+    with torch.no_grad():
+        anomaly_score = discriminator_model(x_tensor)
+    return anomaly_score.item()
+
+# --- STEP 4-7: MAIN TRIAL LOOP ---
+N_TRIALS = 50
+num_epochs = 500
+cgan_auc_scores = [] # <-- Renamed
+
+for trial in range(N_TRIALS):
+    print(f"\n--- CGAN TRIAL {trial+1}/{N_TRIALS} ---") # <-- Renamed
+
+    # --- STEP 4: ARCHITECT THE CGAN ---
+    
+    # 4a. The Classical Generator (G)
+    class ClassicalGenerator(nn.Module):
+        def __init__(self, output_size=16):
+            super(ClassicalGenerator, self).__init__()
+            # A single trainable vector of 16 numbers
+            self.logits = nn.Parameter(torch.randn(1, output_size))
+        def forward(self):
+            # Softmax turns the 16 numbers into a valid probability distribution
+            return torch.softmax(self.logits, dim=1)
+            
+    generator = ClassicalGenerator(output_size=num_qubits**N_DIM)
+
+    # 4b. The Classical Discriminator (D)
+    class Discriminator(nn.Module):
+        def __init__(self, input_size):
+            super(Discriminator, self).__init__()
+            self.linear_input = nn.Linear(input_size, 20)
+            self.leaky_relu = nn.LeakyReLU(0.2)
+            self.linear20 = nn.Linear(20, 1)
+            self.sigmoid = nn.Sigmoid()
+        def forward(self, input: torch.Tensor) -> torch.Tensor:
+            x = self.linear_input(input)
+            x = self.leaky_relu(x)
+            x = self.linear20(x)
+            x = self.sigmoid(x)
+            return x
+    discriminator = Discriminator(input_size=N_DIM)
+
+    # --- STEP 5: DEFINE TRAINING COMPONENTS ---
+    # We use a stable, equal learning rate for the C-GAN
+    lr = 0.005 
+    b1 = 0.7
+    b2 = 0.999
+    generator_optimizer = Adam(generator.parameters(), lr=lr, betas=(b1, b2))
+    discriminator_optimizer = Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))
+
+    g_losses = []
+    d_losses = []
+    rel_ents = []
+
+    # --- STEP 6: RUN THE TRAINING LOOP ---
+    print(f"Starting training for {num_epochs} epochs...")
+    for epoch in range(num_epochs):
+        # Train Discriminator
+        discriminator_optimizer.zero_grad()
+        disc_scores = discriminator(data_samples)
+        gen_dist = generator().reshape(-1, 1) # <-- Fixed: generator() has no input
+        real_loss = adversarial_loss(disc_scores, valid, real_data_dist)
+        fake_loss = adversarial_loss(disc_scores, fake, gen_dist.detach())
+        discriminator_loss = (real_loss + fake_loss) / 2
+        discriminator_loss.backward()
+        discriminator_optimizer.step()
+        # Train Generator
+        generator_optimizer.zero_grad()
+        gen_dist = generator().reshape(-1, 1) # <-- Fixed: generator() has no input
+        disc_scores = discriminator(data_samples)
+        generator_loss = adversarial_loss(disc_scores, valid, gen_dist)
+        generator_loss.backward()
+        generator_optimizer.step()
+        # Log Progress
+        if (epoch + 1) % 100 == 0:
+            g_losses.append(generator_loss.item())
+            d_losses.append(discriminator_loss.item())
+            gen_dist_np = gen_dist.detach().numpy().flatten()
+            real_dist_np = real_distribution.flatten()
+            rel_ent = np.sum(real_dist_np * np.log((real_dist_np + 1e-9) / (gen_dist_np + 1e-9)))
+            rel_ents.append(rel_ent)
+    
+    print(f"Trial {trial+1} training complete.")
+
+    # --- Plot Training Progress (NO SHOW) ---
+    fig_train, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+    fig_train.suptitle(f"CGAN Training Progress (Trial {trial+1})") # <-- Renamed
+    ax1.plot(g_losses, label='Generator Loss')
+    ax1.plot(d_losses, label='Discriminator Loss')
+    ax1.set_title("Adversarial Losses")
+    ax1.set_xlabel("Epoch (x100)"); ax1.set_ylabel("Loss"); ax1.legend()
+    ax2.plot(rel_ents, label='Relative Entropy', color='green')
+    ax2.set_title("Relative Entropy (G vs. Real)")
+    ax2.set_xlabel("Epoch (x100)"); ax2.set_ylabel("Entropy"); ax2.legend()
+    
+    plt.savefig(f'plots/CGAN_Trial_{trial+1}_TrainingProgress.png') # <-- Renamed
+    plt.close(fig_train)
+
+    # --- STEP 7: DEPLOYMENT AND VALIDATION---
+    print(f"Running validation for Trial {trial+1}...")
+    healthy_scores = []
+    for i in range(min(1000, len(X_train_healthy))):
+        sample = X_train_healthy.iloc[i].values
+        score = detect_anomaly(sample, scaler, pca, discriminator)
+        healthy_scores.append(score)
+
+    anomalous_scores = []
+    for i in range(len(X_test_anomalous)):
+        sample = X_test_anomalous.iloc[i].values
+        score = detect_anomaly(sample, scaler, pca, discriminator)
+        anomalous_scores.append(score)
+
+    # --- Visualize the Results: Histogram (NO SHOW) ---
+    fig_hist, ax_hist = plt.subplots(figsize=(10, 6))
+    ax_hist.hist(healthy_scores, bins=50, alpha=0.7, label='Healthy Scores (Trained On)', density=True, color='blue')
+    ax_hist.hist(anomalous_scores, bins=50, alpha=0.7, label='Anomalous Scores (NEVER Seen Before)', density=True, color='red')
+    ax_hist.set_title(f"CGAN-AD Score Distributions (Trial {trial+1})") # <-- Renamed
+    ax_hist.set_xlabel("Anomaly Score (1.0 = Real/Healthy, 0.0 = Fake/Anomalous)")
+    ax_hist.set_ylabel("Probability Density"); ax_hist.legend()
+    
+    plt.savefig(f'plots/CGAN_Trial_{trial+1}_Histogram.png') # <-- Renamed
+    plt.close(fig_hist)
+
+    # --- The "Money Plot": The ROC Curve (NO SHOW) ---
+    y_true_healthy = np.zeros(len(healthy_scores))
+    y_true_anomalous = np.ones(len(anomalous_scores))
+    y_true = np.concatenate([y_true_healthy, y_true_anomalous])
+    y_scores = np.concatenate([healthy_scores, anomalous_scores])
+    fpr, tpr, _ = roc_curve(y_true, 1 - np.array(y_scores))
+    roc_auc = auc(fpr, tpr)
+    
+    cgan_auc_scores.append(roc_auc) # <-- Renamed
+    
+    fig_roc, ax_roc = plt.subplots(figsize=(10, 8))
+    ax_roc.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')
+    ax_roc.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+    ax_roc.set_xlim([0.0, 1.0]); ax_roc.set_ylim([0.0, 1.05])
+    ax_roc.set_xlabel('False Positive Rate'); ax_roc.set_ylabel('True Positive Rate')
+    ax_roc.set_title(f'CGAN-AD ROC Curve (Trial {trial+1})'); ax_roc.legend(loc="lower right") # <-- Renamed
+    
+    plt.savefig(f'plots/CGAN_Trial_{trial+1}_ROC.png') # <-- Renamed
+    plt.close(fig_roc)
+    
+    print(f"Trial {trial+1} complete. AUC: {roc_auc:.4f}")
+
+# --- FINAL RESULTS ---
+print("\n" + "="*30)
+print("--- FINAL CGAN RESULTS ---") # <-- Renamed
+print(f"Scores over {N_TRIALS} trials:")
+print([round(score, 4) for score in cgan_auc_scores]) # <-- Renamed
+print(f"\nAverage CGAN AUC: {np.mean(cgan_auc_scores):.4f}") # <-- Renamed
+print(f"Standard Deviation: {np.std(cgan_auc_scores):.4f}") # <-- Renamed
+print(f"Max AUC (Best Run): {np.max(cgan_auc_scores):.4f}") # <-- Renamed
+print(f"Min AUC (Worst Run): {np.min(cgan_auc_scores):.4f}") # <-- Renamed
+print(f"\nAll {N_TRIALS*3} plots saved to 'plots/' directory.")
+print("="*30)
+
+print("\n--- ACTION PLAN COMPLETE ---")