Create demo_ai_sim_quantum_pipeline.py

Author: Web4application

src/demo_ai_sim_quantum_pipeline.py

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+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import openpyxl
+from pathlib import Path
+
+# Quantum imports
+from qiskit import QuantumCircuit, Aer, execute
+
+# Path to Aura workbook
+AURA_FILE = Path("../data/Aura.xlsl")
+
+# ---------- AI + SIMULATION PART ----------
+
+def load_data(sheet_name="SimulationInput"):
+    return pd.read_excel(AURA_FILE, sheet_name=sheet_name)
+
+def run_simulation(params_df, iterations=1000):
+    results = []
+    for _, row in params_df.iterrows():
+        base_growth = row["growth_rate"]
+        population = row["initial_population"]
+
+        sims = []
+        for _ in range(iterations):
+            pop = population
+            for _ in range(row["years"]):
+                pop = pop * (1 + base_growth + np.random.normal(0, 0.01))
+            sims.append(pop)
+        results.append({
+            "scenario": row["scenario"],
+            "expected_population": np.mean(sims),
+            "std_dev": np.std(sims)
+        })
+    return pd.DataFrame(results)
+
+def train_ai_model(results_df, params_df):
+    X = params_df[["growth_rate", "years"]].values
+    y = results_df["expected_population"].values
+    model = LinearRegression().fit(X, y)
+
+    preds = model.predict(X)
+    results_df["ai_prediction"] = preds
+    return results_df, model
+
+def export_results(df, sheet_name):
+    with pd.ExcelWriter(AURA_FILE, engine="openpyxl", mode="a", if_sheet_exists="replace") as writer:
+        df.to_excel(writer, sheet_name=sheet_name, index=False)
+
+# ---------- QUANTUM PART ----------
+
+def load_quantum_instructions(sheet_name="QuantumInput"):
+    """
+    Expected sheet format:
+    | qubits | gate | target | param |
+    | 2      | h    | 0      |       |
+    | 2      | cx   | 0,1    |       |
+    | 2      | rx   | 0      | 1.57  |
+    """
+    return pd.read_excel(AURA_FILE, sheet_name=sheet_name)
+
+def build_and_run_quantum(qdf, shots=1024):
+    num_qubits = int(qdf["qubits"].max())  # assume first row has total qubits
+    qc = QuantumCircuit(num_qubits)
+
+    for _, row in qdf.iterrows():
+        gate = str(row["gate"]).lower()
+        targets = str(row["target"]).split(",")
+        param = row.get("param", None)
+
+        if gate == "h":
+            qc.h(int(targets[0]))
+        elif gate == "x":
+            qc.x(int(targets[0]))
+        elif gate == "rx" and pd.notna(param):
+            qc.rx(float(param), int(targets[0]))
+        elif gate == "cx":
+            qc.cx(int(targets[0]), int(targets[1]))
+
+    qc.measure_all()
+
+    backend = Aer.get_backend("qasm_simulator")
+    job = execute(qc, backend, shots=shots)
+    counts = job.result().get_counts()
+    return counts
+
+def format_quantum_results(counts):
+    df = pd.DataFrame(list(counts.items()), columns=["state", "counts"])
+    return df
+
+# ---------- MAIN ----------
+
+def main():
+    # AI + Simulation
+    print("Loading simulation data...")
+    params_df = load_data()
+    print("Running Monte Carlo simulation...")
+    results_df = run_simulation(params_df)
+    print("Training AI model...")
+    results_df, _ = train_ai_model(results_df, params_df)
+    export_results(results_df, "SimulationResults")
+
+    # Quantum
+    print("Loading quantum instructions...")
+    qdf = load_quantum_instructions()
+    print("Building and running quantum circuit...")
+    counts = build_and_run_quantum(qdf)
+    q_results = format_quantum_results(counts)
+    export_results(q_results, "QuantumResults")
+
+    print("✅ Demo complete: AI + Simulation + Quantum results written to Aura.xlsl")
+
+if __name__ == "__main__":
+    main()