class EuropeanCallDelta(UncertaintyProblem): """ The European Call Option Delta. Evaluates the variance for a European call option given an uncertainty model. The payoff function is f(S, K) = max(0, S - K) for a spot price S and strike price K. """ 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) def required_ancillas(self): num_uncertainty_ancillas = self._uncertainty_model.required_ancillas() num_comparator_ancillas = self._comparator.required_ancillas() num_ancillas = num_uncertainty_ancillas + num_comparator_ancillas return num_ancillas def required_ancillas_controlled(self): num_uncertainty_ancillas = self._uncertainty_model.required_ancillas_controlled( ) num_comparator_ancillas = self._comparator.required_ancillas_controlled( ) num_ancillas_controlled = num_uncertainty_ancillas + num_comparator_ancillas return num_ancillas_controlled def build(self, qc, q, q_ancillas=None, params=None): # get qubit lists q_state = [q[i] for i in self.i_state] q_objective = q[self.i_objective] # apply uncertainty model self._uncertainty_model.build(qc, q_state, q_ancillas) # apply comparator to compare qubit self._comparator.build(qc, q_state + [q_objective], q_ancillas)
class EuropeanCallDelta(UncertaintyProblem): """ The European Call Option Delta. Evaluates the variance for a European call option given an uncertainty model. The payoff function is f(S, K) = max(0, S - K) for a spot price S and strike price K. """ CONFIGURATION = { 'name': 'EuropeanCallDelta', 'description': 'European Call Delta', 'input_schema': { '$schema': 'http://json-schema.org/schema#', 'id': 'ECD_schema', 'type': 'object', 'properties': { 'strike_price': { 'type': 'integer', 'default': 0 }, 'i_state': { 'type': ['array', 'null'], 'items': { 'type': 'integer' }, 'default': None }, 'i_objective': { 'type': ['integer', 'null'], 'default': None } }, 'additionalProperties': False }, 'depends': [ { 'pluggable_type': 'univariate_distribution', 'default': { 'name': 'NormalDistribution' } }, ], } 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 required_ancillas(self): num_uncertainty_ancillas = self._uncertainty_model.required_ancillas() num_comparator_ancillas = self._comparator.required_ancillas() num_ancillas = num_uncertainty_ancillas + num_comparator_ancillas return num_ancillas def required_ancillas_controlled(self): num_uncertainty_ancillas = self._uncertainty_model.required_ancillas_controlled( ) num_comparator_ancillas = self._comparator.required_ancillas_controlled( ) num_ancillas_controlled = num_uncertainty_ancillas + num_comparator_ancillas return num_ancillas_controlled def build(self, qc, q, q_ancillas=None): # get qubit lists q_state = [q[i] for i in self.i_state] q_objective = q[self.i_objective] # apply uncertainty model self._uncertainty_model.build(qc, q_state, q_ancillas) # apply comparator to compare qubit self._comparator.build(qc, q_state + [q_objective], q_ancillas)