qgym.envs.scheduling.scheduling_state module

This module contains the SchedulingState class.

This SchedulingState represents the State of the Scheduling environment.

class qgym.envs.scheduling.scheduling_state.SchedulingState(*, machine_properties, max_gates, dependency_depth, circuit_generator, rulebook)

Bases: State[Dict[str, ndarray[Any, dtype[int32]] | ndarray[Any, dtype[int8]]], ndarray[Any, dtype[int32]]]

The SchedulingState class.

__init__(*, machine_properties, max_gates, dependency_depth, circuit_generator, rulebook)

Init of the SchedulingState class.

Parameters:

• machine_properties (MachineProperties) – A MachineProperties object.

• max_gates (int) – Maximum number of gates allowed in a circuit.

• dependency_depth (int) – Number of dependencies given in the observation. Determines the shape of the dependencies observation, which has the shape (dependency_depth, max_gates).

• circuit_generator (CircuitGenerator) – Generator class for generating circuits for training.

• rulebook (CommutationRulebook) – CommutationRulebook describing the commutation rules.

busy

Amount of cycles that a qubit is still busy (zero if available). Used internally for the hardware limitations.

circuit_info

CircuitInfo` dataclass containing the encoded circuit and attributes used to update the state.

create_observation_space()

Create the corresponding observation space.

Return type:

Dict

Returns:

Observation space in the form of a Dict space containing:

• MultiBinary space representing the legal actions. If the value at index \(i\) determines if gate number \(i\) can be scheduled or not.

• MultiDiscrete space representing the integer encoded gate names.

• MultiDiscrete space representing the interaction of each gate (q1 and q2).

• MultiDiscrete space representing the first \(n\) gates that must be scheduled before this gate.

cycle

Current ‘machine’ cycle.

gates

Dictionary with gate names as keys and GateInfo dataclasses as values.

is_done()

Determine if the state is done or not.

Return type:

bool

Returns:

Boolean value stating whether we are in a final state.

machine_properties

MachineProperties class containing machine properties and limitations.

obtain_info()

Obtain additional information.

Return type:

dict[str, int | ndarray[Any, dtype[int32]]]

Returns:

Optional debugging info for the current state.

obtain_observation()

Obtain an observation based on the current state.

Return type:

dict[str, ndarray[Any, dtype[int32]] | ndarray[Any, dtype[int8]]]

Returns:

Observation based on the current state.

reset(*, seed=None, circuit=None, **_kwargs)

Reset the state and load a new (random) initial state.

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.

• circuit (list[Gate] | None) – Optional list of a circuit for the next episode, each entry in the list should be a Gate. When a circuit is give, no random circuit will be generated.

• _kwargs (Any) – Additional options to configure the reset.

Return type:

SchedulingState

Returns:

Self.

steps_done: int

Number of steps done since the last reset.

update_state(action)

Update the state of this environment using the given action.

Parameters:

action (ndarray[Any, dtype[int32]]) – First entry determines a gate to schedule, the second entry increases the cycle if nonzero.

Return type:

SchedulingState

Returns:

Self.

utils

SchedulingUtils dataclass with a random circuit generator, commutation rulebook and a gate encoder.