def __init__(self,
num_target_qubits: int,
mu: float = 0,
sigma: float = 1,
low: float = 0,
high: float = 1) -> None:
r"""
Univariate lognormal distribution
Args:
num_target_qubits: number of qubits it acts on,
has a min. value of 1.
mu: expected value of considered normal distribution
sigma: standard deviation of considered normal distribution
low: lower bound, i.e., the value corresponding to \|0...0>
(assuming an equidistant grid)
high: upper bound, i.e., the value corresponding to \|1...1>
(assuming an equidistant grid)
"""
validate_min('num_target_qubits', num_target_qubits, 1)
probabilities, _ = UnivariateDistribution.\
pdf_to_probabilities(
lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu)),
low, high, 2 ** num_target_qubits)
super().__init__(num_target_qubits, probabilities, low, high)
def __init__(self, num_target_qubits, mu=0, sigma=1, low=0, high=1):
"""
Constructor.
Univariate lognormal distribution
Args:
num_target_qubits (int): number of qubits it acts on
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)
"""
self.validate(locals())
probabilities, _ = UnivariateDistribution.\
pdf_to_probabilities(lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu)), low, high, 2 ** num_target_qubits)
super().__init__(num_target_qubits, probabilities, low, high)
def lognorm_pdf(mean, sd):
"""Define a log-normal PDF from its mean and standard deviation"""
sigma = np.sqrt(np.log(1 + sd**2 / mean**2))
mu = np.log(mean) - (sigma**2 / 2)
return lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu))
def __init__(self, num_target_qubits, mu=0, sigma=1, low=0, high=1):
self.validate(locals())
probabilities, _ = UnivariateDistribution.\
pdf_to_probabilities(lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu)), low, high, 2 ** num_target_qubits)
super().__init__(num_target_qubits, probabilities, low, high)