mqt.ddsim¶

MQT DDSIM Python Package.

Submodules¶

Package Contents¶

__version__: str¶

class CircuitSimulator(circ: QuantumComputation, approximation_step_fidelity: float = 1.0, approximation_steps: int = 1, approximation_strategy: str = 'fidelity', seed: int = -1)¶

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.

get_tolerance() → float¶: Get the tolerance for the DD package.

set_tolerance(tol: float) → None¶: Set the tolerance for the DD package.

simulate(shots: int) → dict[str, int]¶: Simulate the circuit and return the result as a dictionary of counts.

get_constructed_dd() → VectorDD¶: Get the vector DD resulting from the simulation.

expectation_value(observable: QuantumComputation) → float¶: Compute the expectation value for the given observable.

class DeterministicNoiseSimulator(circ: QuantumComputation, approximation_step_fidelity: float = 1.0, approximation_steps: int = 1, approximation_strategy: str = 'fidelity', seed: int = -1, noise_effects: str = 'APD', noise_probability: float = 0.01, amp_damping_probability: float | None = 0.02, multi_qubit_gate_factor: float = 2)¶

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.

get_tolerance() → float¶: Get the tolerance for the DD package.

set_tolerance(tol: float) → None¶: Set the tolerance for the DD package.

simulate(shots: int) → dict[str, int]¶: Simulate the circuit and return the result as a dictionary of counts.

get_constructed_dd() → VectorDD¶: Get the vector DD resulting from the simulation.

class HybridSimulator(circ: QuantumComputation, approximation_step_fidelity: float = 1.0, approximation_steps: int = 1, approximation_strategy: str = 'fidelity', seed: int = -1, mode: HybridSimulatorMode = ..., nthreads: int = 2)¶

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.

get_tolerance() → float¶: Get the tolerance for the DD package.

set_tolerance(tol: float) → None¶: Set the tolerance for the DD package.

simulate(shots: int) → dict[str, int]¶: Simulate the circuit and return the result as a dictionary of counts.

get_constructed_dd() → VectorDD¶: Get the vector DD resulting from the simulation.

get_mode() → HybridSimulatorMode¶: Get the mode of the hybrid simulator.

get_final_amplitudes() → list[complex]¶: Get the final amplitudes from the hybrid simulation.

class HybridSimulatorMode(*args, **kwds)¶

Bases: enum.Enum

Enumeration of modes for the HybridSimulator.

DD = 0¶

amplitude = 1¶

class PathSimulator(circ: QuantumComputation, config: PathSimulatorConfiguration = ...)¶

class PathSimulator(circ: QuantumComputation, mode: PathSimulatorMode = ..., bracket_size: int = 2, starting_point: int = 0, gate_cost: Sequence[int] = [], seed: int = 0)

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.

get_tolerance() → float¶: Get the tolerance for the DD package.

set_tolerance(tol: float) → None¶: Set the tolerance for the DD package.

simulate(shots: int) → dict[str, int]¶: Simulate the circuit and return the result as a dictionary of counts.

get_constructed_dd() → VectorDD¶: Get the vector DD resulting from the simulation.

set_simulation_path(path: Sequence[tuple[int, int]], assume_correct_order: bool = False) → None¶

Set the simulation path.

Parameters:

path – The components of the simulation path.
assume_correct_order – Whether the provided path is assumed to be in the correct order. Defaults to False.

class PathSimulatorConfiguration¶

Configuration options for the PathSimulator.

property mode: PathSimulatorMode¶: The mode used for determining a simulation path.

property bracket_size: int¶: Size of the brackets one wants to combine.

property starting_point: int¶: Start of the alternating or gate_cost strategy.

property gate_cost: list[int]¶: A list that contains the number of gates which are considered in each step.

property seed: int¶: Seed for the simulator.

json() → dict[str, Any]¶: Get the configuration as a JSON-style dictionary.

class PathSimulatorMode(*args, **kwds)¶

Bases: enum.Enum

Enumeration of modes for the PathSimulator.

sequential = 0¶

pairwise_recursive = 1¶

bracket = 2¶

alternating = 3¶

gate_cost = 4¶

class StochasticNoiseSimulator(circ: QuantumComputation, approximation_step_fidelity: float = 1.0, approximation_steps: int = 1, approximation_strategy: str = 'fidelity', seed: int = -1, noise_effects: str = 'APD', noise_probability: float = 0.01, amp_damping_probability: float | None = 0.02, multi_qubit_gate_factor: float = 2)¶

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.

get_tolerance() → float¶: Get the tolerance for the DD package.

set_tolerance(tol: float) → None¶: Set the tolerance for the DD package.

simulate(shots: int) → dict[str, int]¶: Simulate the circuit and return the result as a dictionary of counts.

get_constructed_dd() → VectorDD¶: Get the vector DD resulting from the simulation.

class UnitarySimulator(circ: QuantumComputation, approximation_step_fidelity: float = 1.0, approximation_steps: int = 1, approximation_strategy: str = 'fidelity', seed: int = -1, mode: UnitarySimulatorMode = ...)¶

get_number_of_qubits() → int¶: Get the number of qubits.

get_name() → str¶: Get the name of the simulator.

statistics() → dict[str, str]¶: Get additional statistics provided by the simulator.

get_active_vector_node_count() → int¶: Get the number of active vector nodes, i.e., the number of vector DD nodes in the unique table with a non-zero reference count.

get_active_matrix_node_count() → int¶: Get the number of active matrix nodes, i.e., the number of matrix DD nodes in the unique table with a non-zero reference count.