| name | quantum-fault-tolerance-benchmark |
| description | Evaluate quantum error-correcting codes under hardware-motivated and biased noise models. Benchmark fault-tolerant quantum computing primitives via noisy stabilizer simulation. Use when: (1) evaluating QEC code performance, (2) designing fault-tolerant quantum circuits, (3) simulating quantum error correction under realistic noise, (4) comparing logical error rates across hardware platforms, (5) FTPrimitiveBench methodology.
|
Quantum Fault Tolerance Benchmark
Evaluate fault-tolerant quantum computing primitives under realistic noise models.
Based on FTPrimitiveBench methodology (arXiv:2605.04049).
Core Components
1. Noise Models
- Biased noise: Depolarizing, dephasing, amplitude damping with bias ratios
- Hardware-motivated: Derived from physical qubit characteristics (T1, T2, gate fidelities)
- Correlated errors: Spatial and temporal correlations matching hardware behavior
2. Error-Correcting Codes
- Surface codes: Planar, rotated, XZZX variants
- Color codes: 2D and 3D topological codes
- LDPC codes: Quantum low-density parity-check codes
- Concatenated codes: Recursive encoding schemes
3. Fault-Tolerant Primitives
- Logical gates: Transversal, lattice surgery, magic state distillation
- State preparation: Logical |0>, |+, magic states
- Measurement: Logical Pauli measurements
- Memory: Idle logical qubit preservation
Workflow
Step 1: Define Noise Model
noise_model = {
"gate_error_rate": 1e-3,
"measurement_error_rate": 1e-3,
"idle_error_rate": 1e-4,
"bias_ratio": 100,
"correlation_length": 5
}
Step 2: Select QEC Code and Distance
Choose code type and code distance d. Logical error rate typically scales as:
p_L ≈ A * (p/p_th)^((d+1)/2)
Step 3: Run Noisy Stabilizer Simulation
- Simulate circuit with noise injection at each operation
- Track syndrome measurements and apply decoder
- Count logical errors over many trials
Step 4: Extract Metrics
- Logical error rate: Fraction of runs with uncorrectable errors
- Threshold: Physical error rate below which increasing d improves p_L
- Overhead: Physical qubits per logical qubit
- Circuit depth: Time to execute FT primitive
Key References
- FTPrimitiveBench: arXiv:2605.04049
- Related: quantum-error-correction, quantum-systems-engineering
Limitations
- Stabilizer simulation limited to Clifford circuits + magic states
- Computational cost scales with number of qubits and circuit depth
- Real hardware may have noise features not captured by models
