qml.noise.exponential_extrapolate¶

exponential_extrapolate(x, y, asymptote=None, eps=1e-06)[source]¶

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

Example:

>>> x = np.linspace(1, 10, 5)
>>> rng = np.random.default_rng(12345)
>>> y = np.exp(-x) + rng.normal(scale=0.1, size=len(x))
>>> qml.noise.exponential_extrapolate(x, y)
np.float64(1.015...)

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qml.noise.exponential_extrapolate¶

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