raw-curve-quantum-fingerprints

name	raw-curve-quantum-fingerprints
description	Quantum cloud platform authentication framework using multi-dimensional quantum fingerprints from raw measurement data. Constructs Mahalanobis-based fingerprints with drift early warning and adversarial detection to verify which physical device executes workloads, preventing hardware substitution attacks. Activation: quantum authentication, cloud verification, hardware fingerprinting, quantum cloud, device authentication, Mahalanobis distance, drift detection, adversarial detection, raw-curve
metadata	{"arxiv_id":"2606.11644","published":"2026-06-10","authors":"Geyuyan Ma, Xiangdong Meng, Yangyang Fei, Zhiqiang Fan, Hanshi Zhao","tags":["quantum","cloud-security","authentication","fingerprinting","hardware-verification","mahalanobis","adversarial-detection"]}

Raw-Curve Quantum Fingerprint Authentication Framework

Problem: Cloud Hardware Substitution

Quantum cloud platforms offer powerful computing but users cannot verify which physical device executes their workload. Malicious adversaries can redirect jobs to substituted or inferior processors.

Solution: Multi-Dimensional Quantum Fingerprints

Construct fingerprints directly from raw measurement data without curve fitting:

1. Collect raw measurement traces from the target quantum device
2. Extract multi-dimensional features across the trace (timing, amplitude, noise characteristics)
3. Compute Mahalanobis distance between reference and test fingerprints
4. Statistical authentication with confidence bounds

Key Components

Mahalanobis Authentication

D² = (x - μ)ᵀ Σ⁻¹ (x - μ)

where μ is the reference fingerprint mean and Σ is the covariance matrix. Unlike Euclidean distance, Mahalanobis accounts for correlations between features.

Drift Early Warning System

• Monitor fingerprint statistics over time
• Detect gradual degradation before authentication fails
• Distinguish natural drift from malicious substitution
• Alert thresholds based on statistical significance

Adversarial Detection

• Identify patterns inconsistent with genuine hardware
• Detect substituted processors via anomaly scoring
• Multi-dimensional analysis catches sophisticated attacks
• Statistical hypothesis testing with controlled false positive rate

Implementation Steps

1. Baseline collection: Gather N reference traces from authentic device
2. Feature extraction: Compute multi-dimensional fingerprint vector per trace
3. Statistical modeling: Estimate μ and Σ from reference set
4. Online verification: Compare new traces via Mahalanobis distance
5. Drift monitoring: Track fingerprint statistics over time windows
6. Alert generation: Trigger warnings when distance exceeds threshold

Advantages

• No curve fitting needed: Works with raw measurement data directly
• Multi-dimensional: Captures more device characteristics than single-metric approaches
• Early warning: Detects drift before complete authentication failure
• Adversarial detection: Identifies both substitution and manipulation attacks
• General framework: Applicable across different quantum hardware types

When to Apply

• Quantum cloud platform security audits
• Hardware-as-a-service verification
• Quantum computing service level agreements
• Multi-tenant quantum computer isolation verification

Pitfalls

• Requires sufficient reference data for stable covariance estimation
• Hardware maintenance/replacement changes the legitimate fingerprint
• Temperature and calibration variations cause natural drift
• False positives increase with small reference set sizes
• Need periodic re-baselining after legitimate hardware changes