Source code for qiskit_finance.circuit.library.probability_distributions.lognormal

# This code is part of a Qiskit project.
# (C) Copyright IBM 2017, 2024.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""The log-normal probability distribution circuit."""

from typing import Tuple, List, Union, Optional
import numpy as np

from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import Initialize, Isometry

from .normal import _check_bounds_valid, _check_dimensions_match

[docs]class LogNormalDistribution(QuantumCircuit): r"""A circuit to encode a discretized log-normal distribution in qubit amplitudes. A random variable :math:`X` is log-normal distributed if .. math:: \log(X) \sim \mathcal{N}(\mu, \sigma^2) for a normal distribution :math:`\mathcal{N}(\mu, \sigma^2)`. The probability density function of the log-normal distribution is defined as .. math:: \mathbb{P}(X = x) = \frac{1}{x\sqrt{2\pi\sigma^2}} e^{-\frac{(\log(x) - \mu)^2}{\sigma^2}} .. note:: The parameter ``sigma`` in this class equals the **variance**, :math:`\sigma^2` and not the standard deviation. This is for consistency with multivariate distributions, where the uppercase sigma, :math:`\Sigma`, is associated with the covariance. This circuit considers the discretized version of :math:`X` on ``2 ** num_qubits`` equidistant points, :math:`x_i`, truncated to ``bounds``. The action of this circuit can be written as .. math:: \mathcal{P}_X |0\rangle^n = \sum_{i=0}^{2^n - 1} \sqrt{\mathbb{P}(x_i)} |i\rangle where :math:`n` is `num_qubits`. .. note:: The circuit loads the **square root** of the probabilities into the qubit amplitudes such that the sampling probability, which is the square of the amplitude, equals the probability of the distribution. This circuit is for example used in amplitude estimation applications, such as finance [1, 2], where customer demand or the return of a portfolio could be modeled using a log-normal distribution. Examples: This class can be used for both univariate and multivariate distributions. >>> from qiskit_finance.circuit.library.probability_distributions import LogNormalDistribution >>> mu = [1, 0.9, 0.2] >>> sigma = [[1, -0.2, 0.2], [-0.2, 1, 0.4], [0.2, 0.4, 1]] >>> circuit = LogNormalDistribution([2, 2, 2], mu, sigma) >>> circuit.num_qubits 6 References: [1]: Gacon, J., Zoufal, C., & Woerner, S. (2020). Quantum-Enhanced Simulation-Based Optimization. `arXiv:2005.10780 <>`_ [2]: Woerner, S., & Egger, D. J. (2018). Quantum Risk Analysis. `arXiv:1806.06893 <>`_ """ def __init__( self, num_qubits: Union[int, List[int]], mu: Optional[Union[float, List[float]]] = None, sigma: Optional[Union[float, List[float]]] = None, bounds: Optional[Union[Tuple[float, float], List[Tuple[float, float]]]] = None, upto_diag: bool = False, name: str = "P(X)", ) -> None: r""" Args: num_qubits: The number of qubits used to discretize the random variable. For a 1d random variable, ``num_qubits`` is an integer, for multiple dimensions a list of integers indicating the number of qubits to use in each dimension. mu: The parameter :math:`\mu` of the distribution. Can be either a float for a 1d random variable or a list of floats for a higher dimensional random variable. sigma: The parameter :math:`\sigma^2` or :math:`\Sigma`, which is the variance or covariance matrix. bounds: The truncation bounds of the distribution as tuples. For multiple dimensions, ``bounds`` is a list of tuples ``[(low0, high0), (low1, high1), ...]``. If ``None``, the bounds are set to ``(0, 1)`` for each dimension. upto_diag: If True, load the square root of the probabilities up to multiplication with a diagonal for a more efficient circuit. name: The name of the circuit. """ _check_dimensions_match(num_qubits, mu, sigma, bounds) _check_bounds_valid(bounds) # set default arguments dim = 1 if isinstance(num_qubits, int) else len(num_qubits) if mu is None: mu = 0 if dim == 1 else [0] * dim if sigma is None: sigma = 1 if dim == 1 else np.eye(dim) # type: ignore[assignment] if bounds is None: bounds = (0, 1) if dim == 1 else [(0, 1)] * dim if isinstance(num_qubits, int): # univariate case inner = QuantumCircuit(num_qubits, name=name) x = np.linspace(bounds[0], bounds[1], num=2**num_qubits) else: # multivariate case inner = QuantumCircuit(sum(num_qubits), name=name) # compute the evaluation points using meshgrid of numpy # indexing 'ij' yields the "column-based" indexing meshgrid = np.meshgrid( *[ np.linspace(bound[0], bound[1], num=2 ** num_qubits[i]) # type: ignore for i, bound in enumerate(bounds) ], indexing="ij", ) # flatten into a list of points x = list(zip(*[grid.flatten() for grid in meshgrid])) # type: ignore # compute the normalized, truncated probabilities probabilities = [] from scipy.stats import multivariate_normal for x_i in x: # map probabilities from normal to log-normal reference: # if np.min(x_i) > 0: det = 1 / probability = multivariate_normal.pdf(np.log(x_i), mu, sigma) * det else: probability = 0 probabilities += [probability] normalized_probabilities = probabilities / np.sum(probabilities) # store as properties self._values = x self._probabilities = normalized_probabilities self._bounds = bounds super().__init__(*inner.qregs, name=name) # use default the isometry (or initialize w/o resets) algorithm to construct the circuit if upto_diag: inner.append(Isometry(np.sqrt(normalized_probabilities), 0, 0), inner.qubits) self.append(inner.to_instruction(), inner.qubits) # Isometry is not a Gate else: initialize = Initialize(np.sqrt(normalized_probabilities)) circuit = initialize.gates_to_uncompute().inverse() inner.compose(circuit, inplace=True) self.append(inner.to_gate(), inner.qubits) @property def values(self) -> np.ndarray: """Return the discretized points of the random variable.""" return self._values @property def probabilities(self) -> np.ndarray: """Return the sampling probabilities for the values.""" return self._probabilities @property def bounds(self) -> Union[Tuple[float, float], List[Tuple[float, float]]]: """Return the bounds of the probability distribution.""" return self._bounds