| name | hardware-aware-qaoa-cybersecurity |
| description | Hardware-aware QAOA methodology for cybersecurity applications. Combines hardware noise modeling, error-mitigated expectation estimation, and application-specific problem formulation for 100+ qubit NISQ processors. |
| metadata | {"arxiv_id":"2606.09469","published":"2026-06-08"} |
Hardware-Aware QAOA for Cybersecurity
Core Concepts
QAOA on real NISQ hardware requires accounting for device-specific noise, connectivity, and calibration drift. This paper demonstrates hardware-aware QAOA on 100+ qubit IBM processors for honeypot traffic partitioning.
Methodology
Hardware-Aware Problem Formulation
- Map cybersecurity problem to QUBO: Formulate honeypot traffic partitioning as QUBO
- Hardware topology awareness: Account for qubit connectivity constraints
- Noise-aware circuit design: Incorporate device-specific error rates into compilation
Error-Mitigated Expectation Estimation
- Readout error mitigation: Apply measurement error mitigation using calibration data
- Zero-noise extrapolation: Scale circuit noise and extrapolate to zero-noise limit
- Sampling optimization: Use efficient sampling strategies to reduce shot count
Activation Keywords
- hardware-aware qaoa
- honeypot traffic partitioning
- cybersecurity quantum optimization
- QAOA on real hardware
- noise-aware quantum algorithm
- NISQ QAOA deployment
Pitfalls
- Hardware calibration drift degrades QAOA performance between calibrations
- SWAP gates for non-connected qubits increase circuit depth exponentially
- Shot count scales as O(1/epsilon^2) for epsilon precision
- 100+ qubit problems require careful qubit selection based on connectivity graph
