| name | routing-anonymity-quantum-cloud |
| category | quantum-security |
| description | Formal framework for backend identifiability and routing anonymity in quantum cloud services with utility-anonymity trade-offs. |
| trigger_words | ["routing anonymity","quantum cloud","backend identifiability","quantum fingerprint","utility-anonymity","Pauli-transfer-matrix","Chernoff rate"] |
Routing Anonymity for Quantum Cloud Services
Methodology from arXiv:2607.05281 (Priestley & Doosti, Jul 2026).
Problem
Cloud-based quantum computing lets users submit circuits to proprietary backends. Noisy finite-shot outputs carry backend-specific fingerprints that can reveal which physical device was used. Providers may want to hide implementation details, but this creates privacy risks.
Solution: Formal Routing Anonymity Framework
Core Concepts
- Backend-Identifiability Game: Formalizes routing anonymity as a security notion for quantum cloud services
- Hypothesis Testing Formulation: Backend identifiability is cast as a statistical hypothesis testing problem
- Chernoff Rate Decay: Under passive i.i.d. access, routing anonymity decays exponentially at the Chernoff rate
- Utility-Anonymity Trade-off: Fundamental limits on removing backend-specific info without degrading usefulness
- Depth Principle: Identifying fingerprints are inherently intermediate-depth phenomena (Pauli-transfer-matrix analysis)
Experimental Findings
- 87-90% classification accuracy between superconducting backends on AWS Braket
- 96-100% classification accuracy across physical platforms (ion-trap vs superconducting)
- Identifiability survives natural forms of post-processing
- Fingerprints are intermediate-depth phenomena (not shallow or deep circuit regimes)
Application
Use when:
- Designing quantum cloud service architectures
- Evaluating backend privacy guarantees
- Analyzing utility-anonymity trade-offs in quantum computing
- Auditing quantum hardware fingerprinting risks
- Implementing quantum circuit routing with privacy guarantees
Security Framework
User Circuit → Backend Execution → Noisy Output →
[Fingerprint Analysis] → Backend Identity (if identifiable)
Anonymity = P(adversary fails to identify backend)
Decays as: exp(-n * Chernoff_rate) where n = number of shots
Depth Principle
Shallow circuits: noise dominates, no fingerprint
Intermediate circuits: fingerprint emerges (optimal for identification)
Deep circuits: noise washes out, fingerprint fades
