示例#1
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    def is_gate_executable(self, gate: Gate, qubit_mapping: dict) -> bool:
        """Determines if a 2 qubut gate is executable, i.e., whether the gate acts on qubits which are connected in
        the coupling graph

        Args:
            gate (Gate): input 2 qubit gate
            qubit_mapping (dict): logical to physical qubit mapping

        Returns:
            bool: True if the input gate acts on connected qubits. False otherwise
        """
        gate_qubits = list(gate.get_qubits())
        logical_qubit_1, logical_qubit_2 = gate_qubits[0], gate_qubits[1]
        physical_qubit_1, physical_qubit_2 = qubit_mapping.get(
            logical_qubit_1), qubit_mapping.get(logical_qubit_2)
        return self.coupling_graph.has_edge(physical_qubit_1, physical_qubit_2)
示例#2
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    def update_decay_parameter(self, min_score_swap_gate: Gate,
                               decay_parameter: list) -> list:
        """Updates decay parameters for qubits on which SWAP gate with the minimum heuristic score is applied

        Args:
            min_score_swap_gate (Gate): SWAP gate with the minimum heuristic score
            decay_parameter (list): decay parameter list to be updated

        Returns:
            list: updated decay parameter list
        """
        min_score_swap_qubits = list(min_score_swap_gate.get_qubits())
        decay_parameter[min_score_swap_qubits[0]] = decay_parameter[
            min_score_swap_qubits[0]] + 0.001
        decay_parameter[min_score_swap_qubits[1]] = decay_parameter[
            min_score_swap_qubits[1]] + 0.001
        return decay_parameter
示例#3
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    def update_initial_mapping(self, swap_gate: Gate,
                               qubit_mapping: dict) -> dict:
        """Update qubit mapping between logical and physical if a SWAP gate is inserted in the program

        Args:
            swap_gate (Gate): a pyquil SWAP gate
            qubit_mapping (dict): a dictionary containing logical to physical qubit mapping

        Returns:
            dict: updated qubit mapping
        """
        temp_mapping = qubit_mapping.copy()
        swap_qubits = list(swap_gate.get_qubits())
        p_qubit_1, p_qubit_2 = qubit_mapping.get(
            swap_qubits[0]), qubit_mapping.get(swap_qubits[1])
        p_qubit_1, p_qubit_2 = p_qubit_2, p_qubit_1
        temp_mapping.update({
            swap_qubits[0]: p_qubit_1,
            swap_qubits[1]: p_qubit_2
        })
        return temp_mapping
示例#4
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def heuristic_function(F: list, circuit_dag: DiGraph, initial_mapping: dict,
                       distance_matrix: np.matrix, swap_gate: Gate,
                       decay_parameter: list) -> float:
    """Computes a heuristic cost function that is used to rate a candidate SWAP to determine whether the SWAP gate can be inserted in a program to resolve
        qubit dependencies

    Args:
        F (list): list of gates that have no unexecuted predecessors in the DAG
        circuit_dag (DiGraph): a directed acyclic graph representing qubit dependencies between
                                gates
        initial_mapping (dict): a dictionary containing logical to physical qubit mapping
        distance_matrix (np.matrix): represents qubit connections from given coupling graph
        swap_gate (Gate): candidate SWAP gate
        decay_parameter (list): decay parameters for each logical qubit in the mapping

    Returns:
        float: heuristic score for the candidate SWAP gate
    """
    E = create_extended_successor_set(F, circuit_dag)
    min_score_swap_qubits = list(swap_gate.get_qubits())
    size_E = len(E)
    size_F = len(F)
    W = 0.5
    max_decay = max(decay_parameter[min_score_swap_qubits[0]],
                    decay_parameter[min_score_swap_qubits[1]])
    f_distance = 0
    e_distance = 0
    for gate_details in F:
        f_distance += calculate_distance(gate_details, distance_matrix,
                                         initial_mapping)

    for gate_details in E:
        e_distance += calculate_distance(gate_details, distance_matrix,
                                         initial_mapping)

    f_distance = f_distance / size_F
    e_distance = W * (e_distance / size_E)
    H = max_decay * (f_distance + e_distance)
    return H