| 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:
- Collect raw measurement traces from the target quantum device
- Extract multi-dimensional features across the trace (timing, amplitude, noise characteristics)
- Compute Mahalanobis distance between reference and test fingerprints
- 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
- Baseline collection: Gather N reference traces from authentic device
- Feature extraction: Compute multi-dimensional fingerprint vector per trace
- Statistical modeling: Estimate μ and Σ from reference set
- Online verification: Compare new traces via Mahalanobis distance
- Drift monitoring: Track fingerprint statistics over time windows
- 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
