NormalDistribution

class NormalDistribution(num_target_qubits, mu=0, sigma=1, low=- 1, high=1)

The Univariate Normal Distribution.

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

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

Methods

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