qml.transforms.exponential_extrapolate

exponential_extrapolate(x, y, asymptote=None, eps=1e-06)

Extrapolate to the zero-noise limit using an exponential model ($Ae^{Bx} + C$). This is done by linearizing the data using a logarithm, whereupon a linear fit is performed. Once the model parameters are found, they are transformed back to exponential parameters.

Parameters

• x (Array) – Data in x axis.

• y (Array) – Data in y axis such that $y = f(x)$.

• asymptote (float) – Infinite noise limit expected for your circuit of interest ($C$ in the equation above). Defaults to 0 in the case an asymptote is not supplied.

• eps (float) – Epsilon to regularize $\log(y - C)$ when the argument is to close to zero or negative.

Returns

Extrapolated value at f(0).

Return type

float

See also

richardson_extrapolate(), mitigate_with_zne().

Example:

>>> np.random.seed(0)
>>> x = np.linspace(1, 10, 5)
>>> y = np.exp(-x) + np.random.normal(scale=0.1, size=len(x))
>>> qml.transforms.exponential_extrapolate(x, y)
0.23365009000522544

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