qoolqit

package

qoolqit

A Python library for algorithm development in the Rydberg Analog Model.

Classes

DataGraph — The main graph structure to represent problem data.
InteractionEmbedder — A matrix to graph embedder using the interaction embedding algorithm.
InteractionEmbeddingConfig — Configuration parameters for the interaction embedding.
SpringLayoutConfig — Configuration parameters for the spring-layout embedding.
SpringLayoutEmbedder — A graph to graph embedder using the spring layout algorithm.
BladeConfig — Configuration parameters to embed with BLaDE.
Blade — A matrix to graph embedder using the BLaDE algorithm.
Blackman — A Blackman window of a specified duration and area under the curve.
Constant — A constant waveform over a given duration.
Delay — An empty waveform.
Interpolated — A waveform created from interpolation of a set of data points.
PiecewiseLinear — A piecewise linear waveform.
Ramp — A ramp that linearly interpolates between an initial and final value.
Sin — An arbitrary sine over a given duration.
Drive — The drive Hamiltonian acting over a duration.
Register — The Register in QoolQit, representing a set of qubits with coordinates.
QuantumProgram — A program representing a Sequence acting on a Register of qubits.
CompilerProfile
SequenceCompiler — Compiles a QoolQit Register and Drive to a Device.
AnalogDevice — A realistic device for analog sequence execution.
DigitalAnalogDevice — A device with digital and analog capabilities.
MockDevice — A virtual device for unconstrained prototyping.
Device — QoolQit Device wrapper around a Pulser BaseDevice.

Functions

available_default_devices — Show the default available devices in QooQit.

class
DataGraph (edges: Iterable = [])

Bases : BaseGraph

The main graph structure to represent problem data.

Default constructor for the BaseGraph.

Parameters

edges : Iterable — set of edge tuples (i, j)

Attributes

adj — Graph adjacency object holding the neighbors of each node.
name — String identifier of the graph.
nodes — A NodeView of the Graph as G.nodes or G.nodes().
edges — An EdgeView of the Graph as G.edges or G.edges().
degree — A DegreeView for the Graph as G.degree or G.degree().
sorted_edges : set — Returns the set of edges (u, v) such that (u < v).
all_node_pairs : set — Return a list of all possible node pairs in the graph.
has_coords : bool — Check if the graph has coordinates.
has_edges : bool — Check if the graph has edges.
coords : dict — Return the dictionary of node coordinates.
node_weights : dict — Return the dictionary of node weights.
edge_weights : dict — Return the dictionary of edge weights.
has_node_weights : bool — Check if the graph has node weights.
has_edge_weights : bool — Check if the graph has edge weights.

Methods

line — Constructs a line graph, with the respective coordinates.
circle — Constructs a circle graph, with the respective coordinates.
random_er — Constructs an Erdős–Rényi random graph.
triangular — Constructs a triangular lattice graph, with respective coordinates.
hexagonal — Constructs a hexagonal lattice graph, with respective coordinates.
heavy_hexagonal — Constructs a heavy-hexagonal lattice graph, with respective coordinates.
square — Constructs a square lattice graph, with respective coordinates.
random_ud — Constructs a random unit-disk graph.
from_matrix — Constructs a graph from a symmetric square matrix.
set_ud_edges — Reset the set of edges to be equal to the set of unit-disk edges.

classmethod
line (n: int, spacing: float = 1.0) → DataGraph

Constructs a line graph, with the respective coordinates.

Parameters

n : int — number of nodes.
spacing : float — distance between each node.

classmethod
circle (n: int, spacing: float = 1.0, center: tuple = (0.0, 0.0)) → DataGraph

Constructs a circle graph, with the respective coordinates.

Parameters

n : int — number of nodes.
spacing : float — distance between each node.
center : tuple — point (x, y) to set as the center of the graph.

classmethod
random_er (n: int, p: float, seed: int | None = None) → DataGraph

Constructs an Erdős–Rényi random graph.

Parameters

n : int — number of nodes.
p : float — probability that any two nodes connect.
seed : int | None — random seed.

classmethod
triangular (m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a triangular lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of triangles.
n : int — Number of columns of triangles.
spacing : float — The distance between adjacent nodes on the final lattice.

classmethod
hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a hexagonal lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of hexagons.
n : int — Number of columns of hexagons.
spacing : float — The distance between adjacent nodes on the final lattice.

classmethod
heavy_hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a heavy-hexagonal lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of hexagons.
n : int — Number of columns of hexagons.
spacing : float — The distance between adjacent nodes on the final lattice.

classmethod
square (m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a square lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of square.
n : int — Number of columns of square.
spacing : float — The distance between adjacent nodes on the final lattice.

classmethod
random_ud (n: int, radius: float = 1.0, L: float | None = None) → DataGraph

Constructs a random unit-disk graph.

The nodes are sampled uniformly from a square of size (L x L). If L is not given, it is estimated based on a rough heuristic that of packing N nodes on a square of side L such that the expected minimum distance is R, leading to L ~ (R / 2) * sqrt(π * n).

Parameters

n : int — number of nodes.
radius : float — radius to use for defining the unit-disk edges.
L : float | None — size of the square on which to sample the node coordinates.

classmethod
from_matrix (data: ArrayLike) → DataGraph

Constructs a graph from a symmetric square matrix.

The diagonal values are set as the node weights. For each entry (i, j) where M[i, j] != 0 an edge (i, j) is added to the graph and the value M[i, j] is set as its weight.

Parameters

data : ArrayLike — symmetric square matrix.

Raises

ValueError

property
node_weights : dict

Return the dictionary of node weights.

property
edge_weights : dict

Return the dictionary of edge weights.

property
has_node_weights : bool

Check if the graph has node weights.

Requires all nodes to have a weight.

property
has_edge_weights : bool

Check if the graph has edge weights.

Requires all edges to have a weight.

method
set_ud_edges (radius: float) → None

Reset the set of edges to be equal to the set of unit-disk edges.

class
InteractionEmbedder ()

Bases : MatrixToGraphEmbedder[InteractionEmbeddingConfig]

A matrix to graph embedder using the interaction embedding algorithm.

Attributes

config : ConfigType — Returns the config for the embedding algorithm.
algorithm : Callable — Returns the callable to the embedding algorithm.
info : str — Prints info about the embedding algorithm.

dataclass
InteractionEmbeddingConfig (method: str = 'Nelder-Mead', maxiter: int = 200000, tol: float = 1e-08)

Bases : EmbeddingConfig

Configuration parameters for the interaction embedding.

dataclass
SpringLayoutConfig (iterations: int = 100, threshold: float = 0.0001, seed: int | None = None)

Bases : EmbeddingConfig

Configuration parameters for the spring-layout embedding.

class
SpringLayoutEmbedder (config: SpringLayoutConfig = SpringLayoutConfig())

Bases : GraphToGraphEmbedder[SpringLayoutConfig]

A graph to graph embedder using the spring layout algorithm.

Inits SpringLayoutEmbedder.

Attributes

config : ConfigType — Returns the config for the embedding algorithm.
algorithm : Callable — Returns the callable to the embedding algorithm.
info : str — Prints info about the embedding algorithm.

dataclass
BladeConfig (max_min_dist_ratio: float | None = None, dimensions: tuple[int, ...] = (5, 4, 3, 2, 2, 2), starting_positions: np.ndarray | None = None, pca: bool = False, steps_per_round: int = 200, compute_weight_relative_threshold: Callable[[float], float] = lambda _: 0.1, compute_max_distance_to_walk: Callable[[float, float | None], float | tuple[float, float, float]] = lambda x, max_radial_dist: np.inf, starting_ratio_factor: int = 2, draw_steps: bool | list[int] = False, device: InitVar[Device | None] = None)

Bases : EmbeddingConfig

Configuration parameters to embed with BLaDE.

class
Blade (config: BladeConfig = BladeConfig())

Bases : MatrixToGraphEmbedder[BladeConfig]

A matrix to graph embedder using the BLaDE algorithm.

Inits Blade.

Parameters

config : BladeConfig — configuration object for the BLaDE algorithm.

Attributes

config : ConfigType — Returns the config for the embedding algorithm.
algorithm : Callable — Returns the callable to the embedding algorithm.
info : str — Prints info about the embedding algorithm.

Methods

embed — Return a DataGraph with coordinates that embeds the input matrix.

method
embed (data: np.ndarray) → DataGraph

Return a DataGraph with coordinates that embeds the input matrix.

Validates the input, runs the embedding algorithm, and validates the output.

Parameters

data : np.ndarray — the matrix to embed into a DataGraph with coordinates.

class
Blackman (duration: float, area: float)

Bases : Waveform

A Blackman window of a specified duration and area under the curve.

Implements the Blackman window shaped waveform blackman(t) = A(0.42 - 0.5cos(αt) + 0.08cos(2αt)) A = area/(0.42duration) α = 2π/duration

Initializes a new Blackman waveform.

See

https://en.wikipedia.org/wiki/Window_function#:~:text=Blackman%20window (external)

Parameters

duration : float — The waveform duration.
area : float — The integral of the waveform.

Example

blackman_wf = Blackman(100.0, area=3.14)

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

method
function (t: float) → float

method
max () → float

method
min () → float

class
Constant (duration: float, value: float)

Bases : Waveform

A constant waveform over a given duration.

Parameters

duration : float — the total duration.
value : float — the value to take during the duration.

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

method
function (t: float) → float

method
max () → float

method
min () → float

class
Delay (duration: float, *args: float, **kwargs: float | np.ndarray)

Bases : Waveform

An empty waveform.

Initializes the Waveform.

Parameters

duration : float — the total duration of the waveform.

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

method
function (t: float) → float

method
max () → float

method
min () → float

class
Interpolated (duration: float, values: ArrayLike, times: Optional[ArrayLike] = None, interpolator: str = 'PchipInterpolator', **interpolator_kwargs: Any)

Bases : Waveform

A waveform created from interpolation of a set of data points.

Initializes a new Interpolated waveform.

Parameters

duration : int — The waveform duration (in ns).
values : ArrayLike — Values of the interpolation points. Must be a list of castable to float or a parametrized object.
times : ArrayLike — Fractions of the total duration (between 0 and 1), indicating where to place each value on the time axis. Must be a list of castable to float or a parametrized object. If not given, the values are spread evenly throughout the full duration of the waveform.
interpolator : str — The SciPy interpolation class to use. Supports "PchipInterpolator" and "interp1d".

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

method
function (t: float) → float

method
min () → float

method
max () → float

class
PiecewiseLinear (durations: list | tuple, values: list | tuple)

Bases : CompositeWaveform

A piecewise linear waveform.

Creates a composite waveform of N ramps that linearly interpolate through the given N+1 values.

Parameters

durations : list | tuple — list or tuple of N duration values.
values : list | tuple — list or tuple of N+1 waveform values.

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.
durations : list[float] — Returns the list of durations of each individual waveform.
times : list[float] — Returns the list of times when each individual waveform starts.
waveforms : list[Waveform] — Returns a list of the individual waveforms.
n_waveforms : int — Returns the number of waveforms.

class
Ramp (duration: float, initial_value: float, final_value: float)

Bases : Waveform

A ramp that linearly interpolates between an initial and final value.

Parameters

duration : float — the total duration.
initial_value : float — the initial value at t = 0.
final_value : float — the final value at t = duration.

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

method
function (t: float) → float

method
max () → float

method
min () → float

class
Sin (duration: float, amplitude: float = 1.0, omega: float = 1.0, phi: float = 0.0, shift: float = 0.0)

Bases : Waveform

An arbitrary sine over a given duration.

Parameters

duration : float — the total duration.
amplitude : float — the amplitude of the sine wave.
omega : float — the frequency of the sine wave.
phi : float — the phase of the sine wave.
shift : float — the vertical shift of the sine wave.

Attributes

duration : float — Returns the duration of the waveform.
params : dict[str, float | np.ndarray] — Dictionary of parameters used by the waveform.

Methods

function

method
function (t: float) → float

class
Drive (*args: Any, amplitude: Waveform | None = None, detuning: Waveform | None = None, weighted_detunings: list[WeightedDetuning] | None = None, phase: float = 0.0)

The drive Hamiltonian acting over a duration.

Default constructor for the Drive.

Must be instantiated with keyword arguments. Accepts either an amplitude waveform, a detuning waveform, or both. A phase value can also be passed.

Parameters

amplitude : Waveform | None — waveform representing Ω(t) in the drive Hamiltonian.
detuning : Waveform | None — waveform representing δ(t) in the drive Hamiltonian.
phase : float — phase value ɸ for the amplitude term.
weighted_detunings : list[WeightedDetuning] | None — additional waveforms and weights applied to individual qubits. Note that these detunings are not supported on all devices.

Attributes

amplitude : Waveform — The amplitude waveform in the drive.
detuning : Waveform — The detuning waveform in the drive.
weighted_detunings : Sequence[WeightedDetuning] — Detunings applied to individual qubits.
phase : float — The phase value in the drive.

Methods

draw

property
amplitude : Waveform

The amplitude waveform in the drive.

property
detuning : Waveform

The detuning waveform in the drive.

property
weighted_detunings : Sequence[WeightedDetuning]

Detunings applied to individual qubits.

property
phase : float

The phase value in the drive.

property
duration : float

method
draw (n_points: int = 500, return_fig: bool = False) → Figure | None

class
Register (qubits: dict)

The Register in QoolQit, representing a set of qubits with coordinates.

Default constructor for the Register.

Parameters

qubits : dict — a dictionary of qubits and respective coordinates {q: (x, y), ...}.

Attributes

qubits : dict — Returns the dictionary of qubits and respective coordinates.
qubits_ids : list — Returns the qubit keys.
n_qubits : int — Number of qubits in the Register.

Methods

from_graph — Initializes a Register from a graph that has coordinates.
from_coordinates — Initializes a Register from a list of coordinates.
distances — Distance between each qubit pair.
min_distance — Minimum distance between all qubit pairs.
radial_distances — Radial distance of each qubit from the origin.
max_radial_distance — Maximum radial distance between all qubits.
interactions — Interaction 1/r^6 between each qubit pair.
draw — Draw the register.

classmethod
from_graph (graph: DataGraph) → Register

Initializes a Register from a graph that has coordinates.

Parameters

graph : DataGraph — a DataGraph instance.

Raises

ValueError

classmethod
from_coordinates (coords: list) → Register

Initializes a Register from a list of coordinates.

Parameters

coords : list — a list of coordinates [(x, y), ...]

Raises

TypeError

property
qubits : dict

Returns the dictionary of qubits and respective coordinates.

property
qubits_ids : list

Returns the qubit keys.

property
n_qubits : int

Number of qubits in the Register.

method
distances () → dict

Distance between each qubit pair.

method
min_distance () → float

Minimum distance between all qubit pairs.

method
radial_distances () → dict

Radial distance of each qubit from the origin.

method
max_radial_distance () → float

Maximum radial distance between all qubits.

method
interactions () → dict

Interaction 1/r^6 between each qubit pair.

method
draw (return_fig: bool = False) → plt.Figure | None

Draw the register.

Parameters

return_fig : bool — boolean argument to return the plt.Figure instance.

class
QuantumProgram (register: Register, drive: Drive)

A program representing a Sequence acting on a Register of qubits.

Parameters

register : Register — the register of qubits, defining their positions.
drive : Drive — the drive acting on qubits, defining amplitude, detuning and phase.

Attributes

register : Register — The register of qubits.
drive : Drive — The driving waveforms.
is_compiled : bool — Check if the program has been compiled.
compiled_sequence : PulserSequence — The Pulser sequence compiled to a specific device.

Methods

compile_to — Compiles the given program to a device.
draw

property
register : Register

The register of qubits.

property
drive : Drive

The driving waveforms.

property
is_compiled : bool

Check if the program has been compiled.

property
compiled_sequence : PulserSequence

The Pulser sequence compiled to a specific device.

method
compile_to (device: Device, profile: CompilerProfile = CompilerProfile.DEFAULT) → None

Compiles the given program to a device.

Parameters

device : Device — the Device to compile to.
profile : CompilerProfile — the compiler profile to use during compilation.

method
draw (n_points: int = 500, compiled: bool = False, return_fig: bool = False) → Figure | None

Raises

ValueError

enum
CompilerProfile (*args, **kwds)

Bases : StrEnum

Attributes

DEFAULT
MAX_AMPLITUDE
MAX_DURATION
MIN_DISTANCE

class
SequenceCompiler (register: Register, drive: Drive, device: Device)

Compiles a QoolQit Register and Drive to a Device.

Initializes the compiler.

Parameters

register : Register — the QoolQit Register.
drive : Drive — the QoolQit Drive.
device : Device — the QoolQit Device.

Attributes

profile : CompilerProfile — The compiler profile to use.

Methods

compile_sequence

property
register : Register

property
drive : Drive

property
device : Device

property
profile : CompilerProfile

The compiler profile to use.

method
compile_sequence () → PulserSequence

Raises

error
CompilationError

available_default_devices () → None

Show the default available devices in QooQit.

class
AnalogDevice ()

Bases : Device

A realistic device for analog sequence execution.

Attributes

specs : dict[str, float | None] — Return the device specification constraints.
energy_ratio : float | None — Return the ratio between the max amplitude and max interaction energy on this device.

class
DigitalAnalogDevice ()

Bases : Device

A device with digital and analog capabilities.

Attributes

specs : dict[str, float | None] — Return the device specification constraints.
energy_ratio : float | None — Return the ratio between the max amplitude and max interaction energy on this device.

class
MockDevice ()

Bases : Device

A virtual device for unconstrained prototyping.

Attributes

specs : dict[str, float | None] — Return the device specification constraints.
energy_ratio : float | None — Return the ratio between the max amplitude and max interaction energy on this device.

class
Device (pulser_device: BaseDevice, default_converter: Optional[UnitConverter] = None)

QoolQit Device wrapper around a Pulser BaseDevice.

Parameters

pulser_device : BaseDevice — a BaseDevice to build the QoolQit device from.
default_converter : Optional[UnitConverter] — optional unit converter to handle unit conversion. Defaults to the unit converter that rescales energies by the maximum allowed amplitude by the device.

Examples

From Pulser device:

qoolqit_device = Device(pulser_device=pulser_device)

From remote Pulser device:

from pulser_pasqal import PasqalCloud
from qoolqit import Device

# Fetch the remote device from the connection
connection = PasqalCloud()
pulser_fresnel_device = connection.fetch_available_devices()["FRESNEL"]

# Wrap a Pulser device object into a QoolQit Device
fresnel_device = Device(pulser_device=PulserFresnelDevice)

From custom Pulser device:

from dataclasses import replace
from pulser import AnalogDevice
from qoolqit import Device

# Converting the pulser Device object in a VirtualDevice object
VirtualAnalog = AnalogDevice.to_virtual()
# Replacing desired values
ModdedAnalogDevice = replace(
    VirtualAnalog,
    max_radial_distance=100,
    max_sequence_duration=7000
    )

# Wrap a Pulser device object into a QoolQit Device
mod_analog_device = Device(pulser_device=ModdedAnalogDevice)

Attributes

specs : dict[str, float | None] — Return the device specification constraints.
energy_ratio : float | None — Return the ratio between the max amplitude and max interaction energy on this device.

Methods

reset_converter — Resets the unit converter to the default one.
set_time_unit — Changes the unit converter according to a reference time unit.
set_energy_unit — Changes the unit converter according to a reference energy unit.
set_distance_unit — Changes the unit converter according to a reference distance unit.
info — Show the device short description and constraints.
from_connection — Return the specified device from the selected device from a connection.

property
converter : UnitConverter

method
reset_converter () → None

Resets the unit converter to the default one.

method
set_time_unit (time: float) → None

Changes the unit converter according to a reference time unit.

method
set_energy_unit (energy: float) → None

Changes the unit converter according to a reference energy unit.

method
set_distance_unit (distance: float) → None

Changes the unit converter according to a reference distance unit.

property
specs : dict[str, float | None]

Return the device specification constraints.

property
energy_ratio : float | None

Return the ratio between the max amplitude and max interaction energy on this device.

property
name : str

method
info () → None

Show the device short description and constraints.

classmethod
from_connection (connection: RemoteConnection, name: str) → Device

Return the specified device from the selected device from a connection.

Available devices through the provided connection are can be seen with the connection.fetch_available_devices() method.

Parameters

connection : RemoteConnection — connection object to fetch the available devices.
name : str — The name of the desired device.

Example

fresnel_device = Device.from_connection(connection=PasqalCloud(), name="FRESNEL")

Raises

ValueError

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qoolqit.devices

qoolqit

class DataGraph (edges: Iterable = [])

classmethod line (n: int, spacing: float = 1.0) → DataGraph

classmethod circle (n: int, spacing: float = 1.0, center: tuple = (0.0, 0.0)) → DataGraph

classmethod random_er (n: int, p: float, seed: int | None = None) → DataGraph

classmethod triangular (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod heavy_hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod square (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod random_ud (n: int, radius: float = 1.0, L: float | None = None) → DataGraph

classmethod from_matrix (data: ArrayLike) → DataGraph

property node_weights : dict

property edge_weights : dict

property has_node_weights : bool

property has_edge_weights : bool

method set_ud_edges (radius: float) → None

class InteractionEmbedder ()

dataclass InteractionEmbeddingConfig (method: str = 'Nelder-Mead', maxiter: int = 200000, tol: float = 1e-08)

dataclass SpringLayoutConfig (iterations: int = 100, threshold: float = 0.0001, seed: int | None = None)

class SpringLayoutEmbedder (config: SpringLayoutConfig = SpringLayoutConfig())

class Blade (config: BladeConfig = BladeConfig())

method embed (data: np.ndarray) → DataGraph

class Blackman (duration: float, area: float)

method function (t: float) → float

method max () → float

method min () → float

class Constant (duration: float, value: float)

method function (t: float) → float

method max () → float

method min () → float

class Delay (duration: float, *args: float, **kwargs: float | np.ndarray)

method function (t: float) → float

method max () → float

method min () → float

class Interpolated (duration: float, values: ArrayLike, times: Optional[ArrayLike] = None, interpolator: str = 'PchipInterpolator', **interpolator_kwargs: Any)

method function (t: float) → float

method min () → float

method max () → float

class PiecewiseLinear (durations: list | tuple, values: list | tuple)

class Ramp (duration: float, initial_value: float, final_value: float)

method function (t: float) → float

method max () → float

method min () → float

class Sin (duration: float, amplitude: float = 1.0, omega: float = 1.0, phi: float = 0.0, shift: float = 0.0)

method function (t: float) → float

class Drive (*args: Any, amplitude: Waveform | None = None, detuning: Waveform | None = None, weighted_detunings: list[WeightedDetuning] | None = None, phase: float = 0.0)

property amplitude : Waveform

property detuning : Waveform

property weighted_detunings : Sequence[WeightedDetuning]

property phase : float

property duration : float

method draw (n_points: int = 500, return_fig: bool = False) → Figure | None

class Register (qubits: dict)

classmethod from_graph (graph: DataGraph) → Register

classmethod from_coordinates (coords: list) → Register

property qubits : dict

property qubits_ids : list

property n_qubits : int

method distances () → dict

method min_distance () → float

method radial_distances () → dict

method max_radial_distance () → float

method interactions () → dict

method draw (return_fig: bool = False) → plt.Figure | None

class QuantumProgram (register: Register, drive: Drive)

property register : Register

property drive : Drive

property is_compiled : bool

property compiled_sequence : PulserSequence

method compile_to (device: Device, profile: CompilerProfile = CompilerProfile.DEFAULT) → None

method draw (n_points: int = 500, compiled: bool = False, return_fig: bool = False) → Figure | None

enum CompilerProfile (*args, **kwds)

class SequenceCompiler (register: Register, drive: Drive, device: Device)

property register : Register

property drive : Drive

property device : Device

property profile : CompilerProfile

method compile_sequence () → PulserSequence

available_default_devices () → None

class AnalogDevice ()

class
DataGraph (edges: Iterable = [])

classmethod
line (n: int, spacing: float = 1.0) → DataGraph

classmethod
circle (n: int, spacing: float = 1.0, center: tuple = (0.0, 0.0)) → DataGraph

classmethod
random_er (n: int, p: float, seed: int | None = None) → DataGraph

classmethod
triangular (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod
hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod
heavy_hexagonal (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod
square (m: int, n: int, spacing: float = 1.0) → DataGraph

classmethod
random_ud (n: int, radius: float = 1.0, L: float | None = None) → DataGraph

classmethod
from_matrix (data: ArrayLike) → DataGraph

property
node_weights : dict

property
edge_weights : dict

property
has_node_weights : bool

property
has_edge_weights : bool

method
set_ud_edges (radius: float) → None

class
InteractionEmbedder ()

dataclass
InteractionEmbeddingConfig (method: str = 'Nelder-Mead', maxiter: int = 200000, tol: float = 1e-08)

dataclass
SpringLayoutConfig (iterations: int = 100, threshold: float = 0.0001, seed: int | None = None)

class
SpringLayoutEmbedder (config: SpringLayoutConfig = SpringLayoutConfig())

class
Blade (config: BladeConfig = BladeConfig())

method
embed (data: np.ndarray) → DataGraph

class
Blackman (duration: float, area: float)

method
function (t: float) → float

method
max () → float

method
min () → float

class
Constant (duration: float, value: float)

method
function (t: float) → float

method
max () → float

method
min () → float

class
Delay (duration: float, *args: float, **kwargs: float | np.ndarray)

method
function (t: float) → float

method
max () → float

method
min () → float

class
Interpolated (duration: float, values: ArrayLike, times: Optional[ArrayLike] = None, interpolator: str = 'PchipInterpolator', **interpolator_kwargs: Any)

method
function (t: float) → float

method
min () → float

method
max () → float

class
PiecewiseLinear (durations: list | tuple, values: list | tuple)

class
Ramp (duration: float, initial_value: float, final_value: float)

method
function (t: float) → float

method
max () → float

method
min () → float

class
Sin (duration: float, amplitude: float = 1.0, omega: float = 1.0, phi: float = 0.0, shift: float = 0.0)

method
function (t: float) → float

class
Drive (*args: Any, amplitude: Waveform | None = None, detuning: Waveform | None = None, weighted_detunings: list[WeightedDetuning] | None = None, phase: float = 0.0)

property
amplitude : Waveform

property
detuning : Waveform

property
weighted_detunings : Sequence[WeightedDetuning]

property
phase : float

property
duration : float

method
draw (n_points: int = 500, return_fig: bool = False) → Figure | None

class
Register (qubits: dict)

classmethod
from_graph (graph: DataGraph) → Register

classmethod
from_coordinates (coords: list) → Register

property
qubits : dict

property
qubits_ids : list

property
n_qubits : int

method
distances () → dict

method
min_distance () → float

method
radial_distances () → dict

method
max_radial_distance () → float

method
interactions () → dict

method
draw (return_fig: bool = False) → plt.Figure | None

class
QuantumProgram (register: Register, drive: Drive)

property
register : Register

property
drive : Drive

property
is_compiled : bool

property
compiled_sequence : PulserSequence

method
compile_to (device: Device, profile: CompilerProfile = CompilerProfile.DEFAULT) → None

method
draw (n_points: int = 500, compiled: bool = False, return_fig: bool = False) → Figure | None

enum
CompilerProfile (*args, **kwds)

class
SequenceCompiler (register: Register, drive: Drive, device: Device)

property
register : Register

property
drive : Drive

property
device : Device

property
profile : CompilerProfile

method
compile_sequence () → PulserSequence

class
AnalogDevice ()

class
DigitalAnalogDevice ()

class
MockDevice ()