A circuit of G gates, each depolarizing the qubit with probability p, survives coherently with probability (1−p)G. If nothing depolarized, the measurement still reports the ideal expectation value; otherwise the qubit is maximally mixed and contributes 0. So the noisy expectation value at "noise scale" c (the circuit stretched to c·G gates via unitary folding — insert G(c−1) extra gate·gate⁻¹ pairs) is:
⟨O(c)⟩ = ⟨O⟩_ideal · (1 − p)^(c·G) = ⟨O⟩_ideal · b^c
b = (1 − p)^G (measured decay base at c=1)
Zero-noise extrapolation runs the same circuit at several folded scales (c = 1, 2, 3 — never c = 0, that hardware doesn't exist) and fits a curve back through c = 0 to estimate the noiseless value, trading extra circuit executions for a bias-corrected result:
Linear (2 pts): y0 = 2y1 − y2
Richardson (3 pts): y0 = 3y1 − 3y2 + y3
Exponential: ln|y_c| ≈ ln|a| − k·c (least squares)
y0 = a · sign(y1)
- Shots per scale N — each lane samples N independent ±1 measurement outcomes from the Born-rule probability P(+1) = (1+⟨O(c)⟩)/2, so the plotted lane mean carries real statistical scatter, not just the theoretical curve.
- New Shots — redraws the random outcomes at the current noise parameters, showing how much the extrapolated estimate itself fluctuates run to run.
- Real-world relevance: this is the core technique behind Qiskit Runtime's and Mitiq's error mitigation, and the kind of protocol quantum-hardware consortia standardize so results from different labs' noisy processors stay comparable.
This 2D companion plots every sampled shot as a scattered point on a flat chart (noise scale c on the x-axis, expectation value on the y-axis) instead of a rotating 3D scene — easier to read the exact curve shape and scatter at a glance.