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# -*- coding: utf-8 -*-
"""
Ensemble Inference Across Backends
Run edits across multiple backends and compute agreement scores
"""
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import logging
logger = logging.getLogger(__name__)
@dataclass
class BackendResult:
"""Result from a single backend"""
backend_id: str
edit_vector: np.ndarray
output: np.ndarray
confidence: float
latency: float # seconds
success: bool
error_message: Optional[str] = None
@dataclass
class EnsembleResult:
"""Result from ensemble inference"""
edit_vector: np.ndarray
backend_results: List[BackendResult]
consensus_output: np.ndarray
agreement_score: float
reliability_boost: float
agreement_matrix: np.ndarray
best_backend: str
ensemble_confidence: float
class EnsembleInferenceManager:
"""
Run edits across multiple quantum backends and compute agreement scores.
Dashboard Extension:
- Agreement matrix across backends
- Reliability boost from ensemble consensus
"""
def __init__(self):
self.backend_configs = self._initialize_backend_configs()
self.inference_history: List[EnsembleResult] = []
def _initialize_backend_configs(self) -> Dict[str, Dict]:
"""Initialize backend configurations"""
return {
'ibm_manila': {
'qubits': 5,
'error_rate': 0.08,
'gate_fidelity': 0.92,
'coherence_time': 30.0,
'base_latency': 0.05
},
'ibm_washington': {
'qubits': 127,
'error_rate': 0.02,
'gate_fidelity': 0.98,
'coherence_time': 120.0,
'base_latency': 0.15
},
'russian_simulator': {
'qubits': 256,
'error_rate': 0.001,
'gate_fidelity': 0.999,
'coherence_time': 1000.0,
'base_latency': 0.30
},
'ibm_kyoto': {
'qubits': 127,
'error_rate': 0.025,
'gate_fidelity': 0.975,
'coherence_time': 100.0,
'base_latency': 0.12
},
'google_sycamore': {
'qubits': 53,
'error_rate': 0.015,
'gate_fidelity': 0.985,
'coherence_time': 80.0,
'base_latency': 0.08
}
}
def run_ensemble_inference(
self,
edit_vector: np.ndarray,
backend_list: List[str]
) -> EnsembleResult:
"""
Run inference across multiple backends and compute ensemble result.
Args:
edit_vector: Edit vector to apply
backend_list: List of backend IDs (e.g., ['ibm_manila', 'ibm_washington'])
Returns:
EnsembleResult with consensus and agreement metrics
"""
# Run inference on each backend
backend_results = []
for backend_id in backend_list:
result = self._run_single_backend(backend_id, edit_vector)
backend_results.append(result)
# Compute agreement matrix
agreement_matrix = self._compute_agreement_matrix(backend_results)
# Compute consensus output
consensus_output = self._compute_consensus(backend_results)
# Compute overall agreement score
agreement_score = self._compute_overall_agreement(agreement_matrix)
# Compute reliability boost
reliability_boost = self._compute_reliability_boost(
backend_results, agreement_score
)
# Find best backend
best_backend = self._select_best_backend(backend_results)
# Compute ensemble confidence
ensemble_confidence = self._compute_ensemble_confidence(
backend_results, agreement_score
)
result = EnsembleResult(
edit_vector=edit_vector,
backend_results=backend_results,
consensus_output=consensus_output,
agreement_score=agreement_score,
reliability_boost=reliability_boost,
agreement_matrix=agreement_matrix,
best_backend=best_backend,
ensemble_confidence=ensemble_confidence
)
self.inference_history.append(result)
logger.info(
f"Ensemble inference complete: {len(backend_list)} backends, "
f"agreement: {agreement_score:.3f}, boost: {reliability_boost:.3f}"
)
return result
def _run_single_backend(
self, backend_id: str, edit_vector: np.ndarray
) -> BackendResult:
"""Run inference on a single backend"""
config = self.backend_configs.get(backend_id)
if config is None:
logger.warning(f"Unknown backend: {backend_id}")
return BackendResult(
backend_id=backend_id,
edit_vector=edit_vector,
output=np.zeros_like(edit_vector),
confidence=0.0,
latency=0.0,
success=False,
error_message=f"Unknown backend: {backend_id}"
)
# Simulate inference with backend-specific noise
noise_level = config['error_rate']
noise = np.random.randn(*edit_vector.shape) * noise_level
output = edit_vector + noise
# Confidence based on gate fidelity
confidence = config['gate_fidelity']
# Latency based on backend and vector size
latency = config['base_latency'] * (1 + len(edit_vector) / 1000.0)
return BackendResult(
backend_id=backend_id,
edit_vector=edit_vector,
output=output,
confidence=confidence,
latency=latency,
success=True
)
def _compute_agreement_matrix(
self, results: List[BackendResult]
) -> np.ndarray:
"""Compute pairwise agreement matrix between backends"""
n = len(results)
agreement_matrix = np.zeros((n, n))
for i in range(n):
for j in range(n):
if i == j:
agreement_matrix[i, j] = 1.0
else:
# Cosine similarity between outputs
output_i = results[i].output
output_j = results[j].output
if np.linalg.norm(output_i) < 1e-6 or np.linalg.norm(output_j) < 1e-6:
agreement_matrix[i, j] = 0.0
else:
similarity = np.dot(output_i, output_j) / (
np.linalg.norm(output_i) * np.linalg.norm(output_j)
)
# Normalize to [0, 1]
agreement_matrix[i, j] = (similarity + 1.0) / 2.0
return agreement_matrix
def _compute_consensus(
self, results: List[BackendResult]
) -> np.ndarray:
"""Compute consensus output from all backends"""
successful_results = [r for r in results if r.success]
if not successful_results:
return np.zeros_like(results[0].edit_vector)
# Weighted average by confidence
total_confidence = sum(r.confidence for r in successful_results)
if total_confidence < 1e-6:
# Unweighted average
outputs = [r.output for r in successful_results]
return np.mean(outputs, axis=0)
# Confidence-weighted average
consensus = np.zeros_like(successful_results[0].output)
for result in successful_results:
weight = result.confidence / total_confidence
consensus += weight * result.output
return consensus
def _compute_overall_agreement(self, agreement_matrix: np.ndarray) -> float:
"""Compute overall agreement score from matrix"""
# Average of off-diagonal elements
n = agreement_matrix.shape[0]
if n <= 1:
return 1.0
# Sum off-diagonal elements
total = 0.0
count = 0
for i in range(n):
for j in range(n):
if i != j:
total += agreement_matrix[i, j]
count += 1
return total / count if count > 0 else 0.0
def _compute_reliability_boost(
self, results: List[BackendResult], agreement_score: float
) -> float:
"""
Compute reliability boost from ensemble consensus.
Boost is higher when:
- More backends agree
- Individual backends have high confidence
- Agreement score is high
"""
if not results:
return 0.0
# Average individual confidence
avg_confidence = np.mean([r.confidence for r in results if r.success])
# Ensemble size factor
ensemble_factor = min(len(results) / 5.0, 1.0)
# Boost formula
boost = (
0.4 * agreement_score +
0.3 * avg_confidence +
0.3 * ensemble_factor
)
return float(np.clip(boost, 0.0, 1.0))
def _select_best_backend(self, results: List[BackendResult]) -> str:
"""Select best backend based on confidence and success"""
successful_results = [r for r in results if r.success]
if not successful_results:
return results[0].backend_id if results else "none"
# Score by confidence and inverse latency
scores = {}
for result in successful_results:
scores[result.backend_id] = (
0.7 * result.confidence +
0.3 * (1.0 / (1.0 + result.latency))
)
return max(scores, key=scores.get)
def _compute_ensemble_confidence(
self, results: List[BackendResult], agreement_score: float
) -> float:
"""Compute overall ensemble confidence"""
if not results:
return 0.0
# Combine individual confidences with agreement
avg_confidence = np.mean([r.confidence for r in results if r.success])
# Ensemble confidence is boosted by agreement
ensemble_confidence = 0.6 * avg_confidence + 0.4 * agreement_score
return float(np.clip(ensemble_confidence, 0.0, 1.0))
def compare_backends(
self, edit_vectors: List[np.ndarray]
) -> Dict[str, Dict[str, float]]:
"""
Compare all backends across multiple edit vectors.
Returns:
Dict mapping backend_id to performance metrics
"""
backend_stats = {
backend_id: {
'avg_confidence': [],
'avg_latency': [],
'success_rate': []
}
for backend_id in self.backend_configs.keys()
}
for edit_vector in edit_vectors:
for backend_id in self.backend_configs.keys():
result = self._run_single_backend(backend_id, edit_vector)
backend_stats[backend_id]['avg_confidence'].append(result.confidence)
backend_stats[backend_id]['avg_latency'].append(result.latency)
backend_stats[backend_id]['success_rate'].append(1.0 if result.success else 0.0)
# Compute averages
comparison = {}
for backend_id, stats in backend_stats.items():
comparison[backend_id] = {
'avg_confidence': float(np.mean(stats['avg_confidence'])),
'avg_latency': float(np.mean(stats['avg_latency'])),
'success_rate': float(np.mean(stats['success_rate']))
}
return comparison
def get_agreement_heatmap(
self, backend_list: List[str], edit_vector: np.ndarray
) -> Tuple[np.ndarray, List[str]]:
"""
Get agreement heatmap for visualization.
Returns:
Tuple of (agreement_matrix, backend_labels)
"""
result = self.run_ensemble_inference(edit_vector, backend_list)
return result.agreement_matrix, backend_list
def compute_reliability_metrics(self) -> Dict[str, float]:
"""Compute overall reliability metrics from history"""
if not self.inference_history:
return {
'avg_agreement': 0.0,
'avg_reliability_boost': 0.0,
'avg_ensemble_confidence': 0.0
}
return {
'avg_agreement': float(np.mean([
r.agreement_score for r in self.inference_history
])),
'avg_reliability_boost': float(np.mean([
r.reliability_boost for r in self.inference_history
])),
'avg_ensemble_confidence': float(np.mean([
r.ensemble_confidence for r in self.inference_history
]))
}
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