def __init__(self,
uncertainty_model,
strike_price,
c_approx,
i_state=None,
i_compare=None,
i_objective=None):
"""
Constructor.
Args:
uncertainty_model (UnivariateDistribution): uncertainty model for spot price
strike_price (float): strike price of the European option
c_approx (float): approximation factor for linear payoff
i_state (Optional(Union(list, numpy.ndarray))): indices of qubits
representing the uncertainty
i_compare (Optional(int)): index of qubit for comparing spot price to strike price
(enabling payoff or not)
i_objective (Optional(int)): index of qubit for objective function
"""
super().__init__(uncertainty_model.num_target_qubits + 2)
self._uncertainty_model = uncertainty_model
self._strike_price = strike_price
self._c_approx = c_approx
if i_state is None:
i_state = list(range(uncertainty_model.num_target_qubits))
self.i_state = i_state
if i_compare is None:
i_compare = uncertainty_model.num_target_qubits
self.i_compare = i_compare
if i_objective is None:
i_objective = uncertainty_model.num_target_qubits + 1
self.i_objective = i_objective
super().validate(locals())
# map strike price to {0, ..., 2^n-1}
lb = uncertainty_model.low
ub = uncertainty_model.high
self._mapped_strike_price = int(
np.round((strike_price - lb) / (ub - lb) *
(uncertainty_model.num_values - 1)))
# create comparator
self._comparator = FixedValueComparator(
uncertainty_model.num_target_qubits, self._mapped_strike_price)
self.offset_angle_zero = np.pi / 4 * (1 - self._c_approx)
if self._mapped_strike_price < uncertainty_model.num_values - 1:
self.offset_angle = -1 * np.pi / 2 * self._c_approx * self._mapped_strike_price / \
(uncertainty_model.num_values - self._mapped_strike_price - 1)
self.slope_angle = np.pi / 2 * self._c_approx / \
(uncertainty_model.num_values - self._mapped_strike_price - 1)
else:
self.offset_angle = 0
self.slope_angle = 0
def __init__(self,
uncertainty_model,
strike_price,
i_state=None,
i_objective=None):
"""
Constructor.
Args:
uncertainty_model (UnivariateDistribution): uncertainty model for spot price
strike_price (float): strike price of the European option
c_approx (float): approximation factor for linear payoff
i_state (list or array): indices of qubits representing the uncertainty
i_objective (int): index of qubit for objective function
"""
super().__init__(uncertainty_model.num_target_qubits + 1)
self._uncertainty_model = uncertainty_model
self._strike_price = strike_price
if i_state is None:
i_state = list(range(uncertainty_model.num_target_qubits))
self.i_state = i_state
if i_objective is None:
i_objective = uncertainty_model.num_target_qubits
self.i_objective = i_objective
super().validate(locals())
# map strike price to {0, ..., 2^n-1}
lb = uncertainty_model.low
ub = uncertainty_model.high
self._mapped_strike_price = int(
np.ceil((strike_price - lb) / (ub - lb) *
(uncertainty_model.num_values - 1)))
# create comparator
self._comparator = FixedValueComparator(
uncertainty_model.num_target_qubits, self._mapped_strike_price)
def __init__(self,
uncertainty_model: UnivariateDistribution,
strike_price: float,
i_state: Optional[Union[List[int], np.ndarray]] = None,
i_objective: Optional[int] = None) -> None:
"""
Constructor.
Args:
uncertainty_model: uncertainty model for spot price
strike_price: strike price of the European option
i_state: indices of qubits representing the uncertainty
i_objective: index of qubit for objective function
"""
super().__init__(uncertainty_model.num_target_qubits + 1)
self._uncertainty_model = uncertainty_model
self._strike_price = strike_price
if i_state is None:
i_state = list(range(uncertainty_model.num_target_qubits))
self.i_state = i_state
if i_objective is None:
i_objective = uncertainty_model.num_target_qubits
self.i_objective = i_objective
# map strike price to {0, ..., 2^n-1}
lb = uncertainty_model.low
ub = uncertainty_model.high
self._mapped_strike_price = int(
np.ceil((strike_price - lb) / (ub - lb) *
(uncertainty_model.num_values - 1)))
# create comparator
self._comparator = FixedValueComparator(
uncertainty_model.num_target_qubits, self._mapped_strike_price)