LogNormalDistribution¶

class LogNormalDistribution(num_target_qubits, mu=0, sigma=1, low=0, high=1)[source]¶

The Univariate Log-Normal Distribution.

Log-normal distribution, truncated to lower and upper bound and discretized on a grid defined by the number of qubits.

Parameters

num_target_qubits (int) – Number of qubits it acts on, has a minimum value of 1.
mu (float) – Expected value of considered normal distribution
sigma (float) – Standard deviation of considered normal distribution
low (float) – Lower bound, i.e., the value corresponding to |0…0> (assuming an equidistant grid)
high (float) – Upper bound, i.e., the value corresponding to |1…1> (assuming an equidistant grid)

Attributes

`LogNormalDistribution.high`	returns high
`LogNormalDistribution.low`	returns low
`LogNormalDistribution.num_target_qubits`	Returns the number of target qubits
`LogNormalDistribution.num_values`	returns number of values
`LogNormalDistribution.probabilities`	returns probabilities
`LogNormalDistribution.values`	returns values

Methods

`LogNormalDistribution.build`(qc, q[, …])
`LogNormalDistribution.build_controlled`(qc, …)	Adds corresponding controlled sub-circuit to given circuit
`LogNormalDistribution.build_controlled_inverse`(qc, …)	Adds controlled inverse of corresponding sub-circuit to given circuit
`LogNormalDistribution.build_controlled_inverse_power`(qc, …)	Adds controlled, inverse, power of corresponding circuit.
`LogNormalDistribution.build_controlled_power`(qc, …)	Adds controlled power of corresponding circuit.
`LogNormalDistribution.build_inverse`(qc, q[, …])	Adds inverse of corresponding sub-circuit to given circuit
`LogNormalDistribution.build_inverse_power`(qc, …)	Adds inverse power of corresponding circuit.
`LogNormalDistribution.build_power`(qc, q, power)	Adds power of corresponding circuit.
`LogNormalDistribution.get_num_qubits`()	returns number of qubits
`LogNormalDistribution.get_num_qubits_controlled`()	returns number of qubits controlled
`LogNormalDistribution.pdf_to_probabilities`(…)	Takes a probability density function (pdf), and returns a truncated and discretized array of probabilities corresponding to it
`LogNormalDistribution.required_ancillas`()	returns required ancillas
`LogNormalDistribution.required_ancillas_controlled`()	returns required ancillas controlled