quantum-pipeline-integrity

name	quantum-pipeline-integrity
description	Contract-based supervision framework for quantum-classical pipeline integrity verification. Uses behavioral fingerprinting to detect pipeline degradation, component substitution, and silent failures in hybrid quantum-classical ML systems. Activation: quantum pipeline integrity, contract-based supervision, quantum ML verification, behavioral fingerprinting, pipeline monitoring
metadata	{"arxiv_id":"2605.13109","published":"2026-05","tags":["quantum-ML","pipeline-integrity","contract-based","verification","behavioral-fingerprinting"]}

Context

Hybrid quantum-classical ML pipelines are vulnerable to silent failures: component substitution, configuration drift, and quantum hardware degradation. Traditional monitoring misses these because they don't crash — they subtly degrade output quality. Contract-based supervision with behavioral fingerprinting provides continuous integrity verification.

Core Methodology

Step 1: Define Behavioral Contracts

1. Specify expected input/output distributions for each pipeline component
2. Define invariant properties: e.g., "quantum circuit output fidelity ≥ 0.95"
3. Establish baseline behavioral fingerprints using golden runs on reference inputs

Step 2: Implement Fingerprinting

1. Generate deterministic test inputs for each pipeline stage
2. Compute behavioral signatures (output distributions, statistical moments)
3. Store fingerprints in tamper-evident log

Step 3: Continuous Verification

1. On each pipeline execution, compare output fingerprint against baseline
2. Flag deviations exceeding threshold (e.g., KL divergence > 0.1)
3. Alert on contract violations: missing quantum layer, substituted classical component

Step 4: Root Cause Analysis

1. Stage-level isolation: binary search through pipeline stages
2. Quantum hardware check: verify device calibration status
3. Classical component check: verify model weights haven't drifted

Implementation Pattern

# Pseudocode for contract-based pipeline verification
class PipelineIntegrityMonitor:
    def __init__(self, contracts, baselines):
        self.contracts = contracts  # Input/output specifications
        self.baselines = baselines  # Golden fingerprints
    
    def verify(self, pipeline_output, stage_name):
        fingerprint = compute_fingerprint(pipeline_output)
        baseline = self.baselines[stage_name]
        contract = self.contracts[stage_name]
        
        # Check contract compliance
        if not contract.check(pipeline_output):
            return Violation("Contract violated", stage_name)
        
        # Check behavioral fingerprint
        divergence = kl_divergence(fingerprint, baseline)
        if divergence > contract.threshold:
            return Violation(f"Fingerprint drift: {divergence:.4f}", stage_name)
        
        return Pass()

Pitfalls

• Fingerprint noise: Quantum hardware noise causes natural fingerprint variation. Set thresholds above noise floor (measure over 100+ runs).
• Baseline staleness: As quantum hardware improves, baselines become outdated. Refresh baselines quarterly.
• Contract specification burden: Defining contracts for all stages is labor-intensive. Start with critical stages (quantum layer, final classifier).
• False positives: Classical ML model retraining changes fingerprints. Distinguish between authorized updates and unauthorized substitutions.

Verification

1. All pipeline stages have defined contracts with measurable thresholds
2. Fingerprint verification runs on every production execution
3. Violations trigger alerts with stage-level root cause identification
4. End-to-end test: deliberately substitute a component → verify detection within 1 execution