Noise Models

Noise models simulate the imperfections of real quantum hardware within a classical simulator. Real qubits have decoherence (information loss over time), gate errors, measurement errors, and crosstalk. Noise models let you study algorithm robustness before running on expensive hardware.

Qiskit's Aer simulator supports noise models: you define errors per gate and operation, then the simulator applies them probabilistically during circuit execution.

• Depolarizing error: with probability $p$, replace the qubit state with a random state
• Amplitude damping: model decay/relaxation (qubit drops from excited state to ground)
• Phase damping: qubit loses phase coherence
• Measurement error: measurement result flipped with some probability

from qiskit_aer.noise import NoiseModel, depolarizing_error, amplitude_damping_error
from qiskit_aer import AerSimulator
 
# Create a noise model
noise_model = NoiseModel()
 
# Add errors
noise_model.add_all_qubit_quantum_error(
    depolarizing_error(0.05, 1),  # 5% error on single-qubit gates
    ['h', 'x', 'y', 'z', 'rx', 'ry', 'rz']
)
 
noise_model.add_all_qubit_quantum_error(
    depolarizing_error(0.1, 2),   # 10% error on two-qubit gates
    ['cx', 'cz']
)
 
# Add measurement error
noise_model.add_all_qubit_readout_error(0.02)  # 2% mismeasurement
 
# Simulate with noise
sim = AerSimulator(noise_model=noise_model)
result = sim.run(qc, shots=1000).result()
print(result.get_counts())

Realistic models use measured calibration data from real hardware:

from qiskit_ibm_runtime.fake_provider import FakeBogota
 
# Fake backend with realistic noise
backend = FakeBogota()
noise_model = NoiseModel.from_backend(backend)
 
sim = AerSimulator(noise_model=noise_model)
result = sim.run(qc, shots=1000).result()

Simulating with noise helps you design algorithms that are robust to hardware imperfections, a key challenge on current (NISQ) devices.