qgym.envs.initial_mapping.initial_mapping_state module

This module contains the InitialMappingState class.

This InitialMapping represents the State of the InitialMapping environment.

Usage:

>>> from qgym.envs.initial_mapping.initial_mapping_state import InitialMappingState
>>> from qgym.envs.initial_mapping.graph_generation import BasicGraphGenerator
>>> import networkx as nx
>>> connection_graph = nx.grid_graph((3,3))
>>> graph_generator = BasicGraphGenerator(9, 0.5)
>>> state = InitialMappingState(connection_graph, graph_generator)

class qgym.envs.initial_mapping.initial_mapping_state.InitialMappingState(connection_graph, graph_generator)[source]

Bases: State[Dict[str, ndarray[Any, dtype[int32]]], ndarray[Any, dtype[int32]]]

The InitialMappingState class.

__init__(connection_graph, graph_generator)[source]

Init of the InitialMappingState class.

Parameters:

connection_graph (Graph) – networkx Graph representation of the QPU topology. Each node represents a physical qubit and each edge represents a connection in the QPU topology.
graph_generator (GraphGenerator) – Graph generator for generating interaction graphs. This generator is used to generate a new interaction graph when InitialMappingState.reset() is called without an interaction graph.

create_observation_space()[source]

Create the corresponding observation space.

Return type:

Dict

Returns:

Observation space in the form of a Dict space containing the following values if the connection graph has no fidelity information:

MultiDiscrete space representing the mapping.
MultiBinary representing the interaction matrix.

graphs: Dictionary containing the graph and matrix representations of the both the interaction graph and connection graph.

is_done()[source]

Determine if the state is done or not.

Return type:: bool
Returns:: Boolean value stating whether we are in a final state.

is_truncated()[source]

Determine if the episode should be truncated or not.

Return type:: bool
Returns:: Boolean value stating whether the episode should be truncated. The episode is truncated if the number of steps in the current episode is more than 10 times the number of nodes in the connection graph.

mapped_qubits: dict[str, set[int]]: Dictionary with two sets containing mapped physical and logical qubits.

mapping: Array of which the index represents a physical qubit, and the value a virtual qubit. A value of n_nodes + 1 represents the case when nothing is mapped to the physical qubit yet.

mapping_dict: dict[int, int]: Dictionary that maps logical qubits (keys) to physical qubits (values).

property n_nodes: int: The number of physical qubits.

obtain_info()[source]

Obtain additional information.

Return type:: dict[str, Any]
Returns:: Optional debugging info for the current state.

obtain_observation()[source]

Obtain an observation based on the current state.

Return type:: dict[str, ndarray[Any, dtype[int32]]]
Returns:: Observation based on the current state.

reset(*, seed=None, interaction_graph=None, **_kwargs)[source]

Reset the state and set a new interaction graph.

To be used after an episode is finished.

Parameters:

seed (int | None) – Seed for the random number generator, should only be provided (optionally) on the first reset call i.e., before any learning is done.
interaction_graph (Graph | None) – Interaction graph to be used for the next iteration, if
created. (None a random interaction graph will be)
_kwargs (Any) – Additional options to configure the reset.

Return type:

InitialMappingState

Returns:

(self) New initial state.

steps_done: int: Number of steps done since the last reset.

update_state(action)[source]

Update the state (in place) of this environment using the given action.

Parameters:: action (ndarray[Any, dtype[int32]]) – Mapping action to be executed.
Return type:: InitialMappingState
Returns:: Self.