quantum/optimizer.py

from services.logger import app_logger
from typing import Dict, Any
import time
import random

# This is a STUB for a quantum optimizer.
# In a real scenario, this would interface with a quantum computing service
# or a sophisticated classical optimizer that mimics quantum approaches.

def optimize_treatment(patient_data: Dict[str, Any], current_treatments: list, conditions: list) -> Dict[str, Any]:
    """
    Stub for a quantum-inspired treatment optimization.
    
    Args:
        patient_data (Dict[str, Any]): Relevant patient characteristics.
        current_treatments (list): List of current medications/therapies.
        conditions (list): List of diagnosed conditions.

    Returns:
        Dict[str, Any]: A dictionary with optimized treatment suggestions.
    """
    app_logger.info(f"Quantum optimizer called for patient data: {patient_data}, treatments: {current_treatments}, conditions: {conditions}")
    
    # Simulate a complex computation
    time.sleep(random.uniform(1, 3)) # Simulate processing time

    # Mocked optimization logic
    # This would be replaced by actual quantum annealing, VQE, QAOA, etc. calls
    # or complex classical algorithms.
    
    suggestions = []
    confidence_score = random.uniform(0.65, 0.95)

    if "diabetes" in [c.lower() for c in conditions]:
        if "metformin" not in [t.lower() for t in current_treatments]:
            suggestions.append({
                "action": "ADD",
                "medication": "Metformin",
                "dosage": "500mg BID",
                "rationale": "Standard first-line for type 2 diabetes, potentially enhanced by quantum risk modeling."
            })
        else:
            suggestions.append({
                "action": "MONITOR",
                "medication": "Metformin",
                "rationale": "Continue Metformin. Quantum analysis suggests current efficacy."
            })
    
    if "hypertension" in [c.lower() for c in conditions]:
        suggestions.append({
            "action": "CONSIDER_ADD",
            "medication": "Lisinopril",
            "dosage": "10mg QD",
            "rationale": "ACE inhibitor, effective for hypertension. Quantum combinatorial analysis suggests synergy."
        })

    if not suggestions:
        suggestions.append({
            "action": "MAINTAIN",
            "medication": "Current Regimen",
            "rationale": "Quantum analysis indicates current treatment plan is near-optimal or requires further data."
        })

    return {
        "optimized_suggestions": suggestions,
        "confidence": f"{confidence_score:.2f}",
        "summary": f"Based on quantum-inspired analysis of {len(conditions)} conditions and {len(current_treatments)} current treatments, the following adjustments are suggested.",
        "iterations": random.randint(1000, 50000) # Mock "quantum" parameter
    }