{
  "spec_version": "1.4.0-otel",
  "timestamp": "2026-04-24T00:13:03.355457Z",
  "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736",
  "span_id": "00f067aa0ba902b7",
  "severity_text": "WARN",
  "severity_number": 13,
  "event_name": "swarm.agent.lifecycle.deprecated",
  "resource": {
    "service.name": "swarm-orchestrator",
    "service.version": "4.2.0",
    "deployment.environment": "production",
    "node.id": "worker-node-aws-us-east-1a-04"
  },
  "attributes": {
    "agent.id": "QuantumSearchAgent",
    "agent.previous_version": "v29.x",
    "agent.target_version": "v30",
    "deprecation.reason": "Transitioning to v30 architecture.",
    "deprecation.action_required": true
  },
  "metrics": {
    "agent.core_logic.function_count": 9,
    "agent.capabilities.count": 0
  },
  "artifacts": {
    "provenance": {
      "repository": "git+ssh://repo/core/agents/specialized/quantum_search_agent.py",
      "commit_sha": "a1b2c3d4e5f6g7h8i9j0",
      "docstring_hash": "sha256:d8c4b12389a..."
    },
    "extracted_capabilities": [],
    "core_logic_signatures": [
      {
        "name": "__init__",
        "ast_hash": "sha256:b8c8d4a98cbdc290",
        "loc": 4,
        "source": "def __init__(self, config: Dict[str, Any], **kwargs):\n        super().__init__(config, **kwargs)\n        self.search_engine = None\n        self.market_oracle = MarketOracle()"
      },
      {
        "name": "execute",
        "ast_hash": "sha256:7ec5c8179724f49a",
        "loc": 25,
        "source": "async def execute(self, task: str = \"search\", target_n: float = 1e15, n_qubits: int = 6, **kwargs: Any) -> Dict[str, Any]:\n        \"\"\"\n        Executes the quantum search capability.\n\n        Args:\n            task (str): \"search\", \"hybrid_search\", \"market_search\", or \"schedule_optimization\".\n            target_n (float): The size of the search space (default: 10^15).\n            n_qubits (int): The number of qubits for the simulation backend (default: 6).\n            kwargs: Additional parameters.\n\n        Returns:\n            Dict[str, Any]: The search results including probability and optimized schedule.\n        \"\"\"\n        if task == \"search\":\n            return await self.run_quantum_search(target_n, n_qubits, **kwargs)\n        elif task == \"hybrid_search\":\n            return await self.run_hybrid_search(target_n, n_qubits, **kwargs)\n        elif task == \"market_search\":\n            symbol = kwargs.get(\"symbol\", \"BTC\")\n            threshold = kwargs.get(\"threshold\", 0.05)\n            return await self.run_market_anomaly_search(symbol, threshold, n_qubits)\n        elif task == \"schedule_optimization\":\n            return await self.run_schedule_optimization(n_qubits, **kwargs)\n        else:\n            return {\"error\": f\"Unknown task: {task}\"}"
      },
      {
        "name": "run_quantum_search",
        "ast_hash": "sha256:4c9e15e844f86d15",
        "loc": 47,
        "source_omitted_for_brevity": true,
        "note": "In a real telemetry pipeline, large functions can be truncated or omitted entirely in favor of the 'ast_hash' to save network bandwidth and storage costs."
      },
      {
        "name": "run_schedule_optimization",
        "ast_hash": "sha256:1f9b447c5a1e01f8",
        "loc": 23,
        "source": "async def run_schedule_optimization(self, n_qubits: int, **kwargs) -> Dict[str, Any]:\n        \"\"\"\n        Runs comparative optimization with different hyperparameter sets.\n        \"\"\"\n        scenarios = {\n            \"Default\": {\"lr\": 0.05, \"beta1\": 0.9, \"beta2\": 0.999},\n            \"Aggressive\": {\"lr\": 0.2, \"beta1\": 0.8, \"beta2\": 0.9},\n            \"Conservative\": {\"lr\": 0.01, \"beta1\": 0.95, \"beta2\": 0.999}\n        }\n\n        results = {}\n\n        for name, params in scenarios.items():\n            logger.info(f\"Running scenario: {name} with {params}\")\n            # Use a fresh engine for each scenario to avoid state pollution, though optimize resets params internally usually\n            # But optimize() starts from zero params.\n            res = await self.run_quantum_search(target_n=1e15, n_qubits=n_qubits, optimizer_params=params)\n            results[name] = res\n\n        return {\n            \"task\": \"schedule_optimization\",\n            \"scenarios\": results\n        }"
      },
      {
        "name": "run_hybrid_search",
        "ast_hash": "sha256:c598c0431c3acf3a",
        "loc": 20,
        "source": "async def run_hybrid_search(self, target_n: float, n_qubits: int, **kwargs) -> Dict[str, Any]:\n        \"\"\"\n        Runs the full hybrid pipeline: Quantum Sampling -> Classical Verification.\n        \"\"\"\n        # 1. Run the quantum part\n        q_result = await self.run_quantum_search(target_n, n_qubits, **kwargs)\n\n        # 2. Simulate Sampling (getting a batch of candidates)\n        candidates = self._mock_sampling(n_samples=100, fidelity=q_result['metrics']['simulation_fidelity'])\n\n        # 3. Classical Verification\n        verified_match = self.verify_candidates(candidates)\n\n        q_result[\"pipeline\"] = {\n            \"sampled_candidates\": len(candidates),\n            \"verification_status\": \"MATCH_FOUND\" if verified_match else \"NO_MATCH\",\n            \"verified_candidate\": verified_match\n        }\n\n        return q_result"
      },
      {
        "name": "run_market_anomaly_search",
        "ast_hash": "sha256:b6da8cca7eee47f3",
        "loc": 23,
        "source": "async def run_market_anomaly_search(self, symbol: str, threshold: float, n_qubits: int = 10) -> Dict[str, Any]:\n        \"\"\"\n        Searches for market anomalies using the Market Oracle and AVG simulation.\n        \"\"\"\n        logger.info(f\"Scanning market data for {symbol} anomalies (> {threshold*100}%) via AVG...\")\n\n        target_indices, anomaly_data = self.market_oracle.find_anomalies(symbol, threshold)\n\n        if not target_indices:\n            return {\"status\": \"NO_ANOMALIES_FOUND\", \"symbol\": symbol}\n\n        primary_target_idx = target_indices[0]\n\n        q_result = await self.run_quantum_search(target_n=1024, n_qubits=n_qubits, target_index=primary_target_idx)\n\n        q_result[\"market_intelligence\"] = {\n            \"symbol\": symbol,\n            \"anomalies_detected\": len(anomaly_data),\n            \"verified_anomalies\": anomaly_data,\n            \"quantum_speedup\": \"Quadratic (O(sqrt N))\"\n        }\n\n        return q_result"
      },
      {
        "name": "_mock_sampling",
        "ast_hash": "sha256:0c1ab4865fb7361d",
        "loc": 14,
        "source": "def _mock_sampling(self, n_samples: int, fidelity: float) -> List[Dict[str, Any]]:\n        \"\"\"\n        Generates mock candidates based on fidelity.\n        \"\"\"\n        candidates = []\n        has_target = True\n\n        if has_target:\n            candidates.append({\"id\": \"target_hash_x99\", \"energy\": -1.0, \"data\": \"THE_NEEDLE\"})\n\n        for _ in range(n_samples - 1):\n             candidates.append({\"id\": f\"noise_{_}\", \"energy\": -0.2, \"data\": \"haystack_straw\"})\n\n        return candidates"
      },
      {
        "name": "verify_candidates",
        "ast_hash": "sha256:66fb5f2a63e86293",
        "loc": 10,
        "source": "def verify_candidates(self, candidates: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:\n        \"\"\"\n        Simulates O(1) classical verification of the batch.\n        \"\"\"\n        logger.info(f\"Verifying {len(candidates)} candidates classically...\")\n        for c in candidates:\n            if c.get(\"energy\") == -1.0:\n                logger.info(\"Target Verified!\")\n                return c\n        return None"
      },
      {
        "name": "get_skill_schema",
        "ast_hash": "sha256:ca055bec7915f054",
        "loc": 50,
        "source_omitted_for_brevity": true,
        "note": "In a real telemetry pipeline, large functions can be truncated or omitted entirely in favor of the 'ast_hash' to save network bandwidth and storage costs."
      }
    ]
  }
}