make_lognormal_distribution | API References | Fire Opal | References

fireopal.make_lognormal_distribution(uncertainty, mu=None, sigma=None, bounds=None, upto_diag=False)

Create parameters for a log-normal distribution.

If X is log-normally distributed then log(X) is normally distributed.

uncertainty (int or list [ int ]) – The number of discrete points per dimension. Each value must be an integer greater than or equal to 2. Use a list for multivariate distributions.
mu (float or list [ float ] or None , optional) – Mean of log(X). Default is None (interpreted as 0 per dimension).
sigma (float or list [ list [ float ] ] or None , optional) – Variance of log(X). For multivariate distributions this is a covariance matrix. Default is None (interpreted as 1 per dimension).
bounds (tuple [ float , float ] or list [ tuple [ float , float ] ] or None , optional) – Truncation bounds. Default is None (interpreted as (0, 1) per dimension).
upto_diag (bool , optional) – Whether to use an optimized circuit that loads the probability distribution up to a diagonal phase matrix. Setting this to True reduces overall circuit depth, which can improve execution fidelity on noisy hardware. Only use this if your subsequent objective function operations are unaffected by these relative phases. Default is False.

dict[str, Any] – A dictionary containing the parameters of the log-normal distribution, suitable for use with make_monte_carlo_problem().