Pulses

BaseReadoutPulse

Bases: Pulse, ABC

QuAM abstract base component for a general readout pulse.

Readout pulse classes should usually inherit from ReadoutPulse, the exception being when a custom integration weights function is required.

Parameters:

Name	Type	Description	Default
length	int	The length of the pulse in samples.	required
digital_marker	(str, list)	The digital marker to use for the pulse. Default is "ON".	required

Source code in quam/components/pulses.py

@quam_dataclass
class BaseReadoutPulse(Pulse, ABC):
    """QuAM abstract base component for a general  readout pulse.

    Readout pulse classes should usually inherit from `ReadoutPulse`, the
    exception being when a custom integration weights function is required.

    Args:
        length (int): The length of the pulse in samples.
        digital_marker (str, list, optional): The digital marker to use for the pulse.
            Default is "ON".
    """

    operation: ClassVar[str] = "measurement"
    digital_marker: str = "ON"

    # TODO Understand why the thresholds were added.
    threshold: float = None
    rus_exit_threshold: float = None

    _weight_labels: ClassVar[List[str]] = ["iw1", "iw2", "iw3"]

    @property
    def integration_weights_names(self):
        return [f"{self.name}{str_ref.DELIMITER}{name}" for name in self._weight_labels]

    @property
    def integration_weights_mapping(self):
        return dict(zip(self._weight_labels, self.integration_weights_names))

    @abstractmethod
    def integration_weights_function(self) -> Dict[str, List[Tuple[float, int]]]:
        """Abstract method to calculate the integration weights.

        Returns:
            Dict containing keys "real", "imag", "minus_real", "minus_imag".
            Values are lists of tuples of (weight, length) pairs.
        """
        ...

    def _config_add_integration_weights(self, config: dict):
        """Add the integration weights to the config"""
        integration_weights = self.integration_weights_function()

        config["integration_weights"][self.integration_weights_names[0]] = {
            "cosine": integration_weights["real"],
            "sine": integration_weights["minus_imag"],
        }
        config["integration_weights"][self.integration_weights_names[1]] = {
            "cosine": integration_weights["imag"],
            "sine": integration_weights["real"],
        }
        config["integration_weights"][self.integration_weights_names[2]] = {
            "cosine": integration_weights["minus_imag"],
            "sine": integration_weights["minus_real"],
        }

        pulse_config = config["pulses"][self.pulse_name]
        pulse_config["integration_weights"] = self.integration_weights_mapping

    def apply_to_config(self, config: dict) -> None:
        """Adds this readout pulse to the QUA configuration.

        See [`QuamComponent.apply_to_config`][quam.core.quam_classes.QuamComponent.apply_to_config]
        for details.
        """
        super().apply_to_config(config)
        self._config_add_integration_weights(config)

apply_to_config(config)

Adds this readout pulse to the QUA configuration.

See QuamComponent.apply_to_config for details.

Source code in quam/components/pulses.py

def apply_to_config(self, config: dict) -> None:
    """Adds this readout pulse to the QUA configuration.

    See [`QuamComponent.apply_to_config`][quam.core.quam_classes.QuamComponent.apply_to_config]
    for details.
    """
    super().apply_to_config(config)
    self._config_add_integration_weights(config)

integration_weights_function() abstractmethod

Abstract method to calculate the integration weights.

Returns:

Type	Description
Dict[str, List[Tuple[float, int]]]	Dict containing keys "real", "imag", "minus_real", "minus_imag".
Dict[str, List[Tuple[float, int]]]	Values are lists of tuples of (weight, length) pairs.

Source code in quam/components/pulses.py

@abstractmethod
def integration_weights_function(self) -> Dict[str, List[Tuple[float, int]]]:
    """Abstract method to calculate the integration weights.

    Returns:
        Dict containing keys "real", "imag", "minus_real", "minus_imag".
        Values are lists of tuples of (weight, length) pairs.
    """
    ...

DragCosinePulse

Bases: Pulse

Cosine based DRAG pulse that compensate for the leakage and AC stark shift.

These DRAG waveforms has been implemented following the next Refs.: Chen et al. PRL, 116, 020501 (2016) https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.020501 and Chen's thesis https://web.physics.ucsb.edu/~martinisgroup/theses/Chen2018.pdf

Parameters:

Name	Type	Description	Default
length	int	The pulse length in ns.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required
amplitude	float	The amplitude in volts.	required
alpha	float	The DRAG coefficient.	required
anharmonicity	float	f_21 - f_10 - The differences in energy between the 2-1 and the 1-0 energy levels, in Hz.	required
detuning	float	The frequency shift to correct for AC stark shift, in Hz.	required

Source code in quam/components/pulses.py

@quam_dataclass
class DragCosinePulse(Pulse):
    """Cosine based DRAG pulse that compensate for the leakage and AC stark shift.

    These DRAG waveforms has been implemented following the next Refs.:
    Chen et al. PRL, 116, 020501 (2016)
    https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.020501
    and Chen's thesis
    https://web.physics.ucsb.edu/~martinisgroup/theses/Chen2018.pdf

    Args:
        length (int): The pulse length in ns.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
        amplitude (float): The amplitude in volts.
        alpha (float): The DRAG coefficient.
        anharmonicity (float): f_21 - f_10 - The differences in energy between the 2-1
            and the 1-0 energy levels, in Hz.
        detuning (float): The frequency shift to correct for AC stark shift, in Hz.
    """

    axis_angle: float
    amplitude: float
    alpha: float
    anharmonicity: float
    detuning: float = 0.0

    def __post_init__(self) -> None:
        return super().__post_init__()

    def waveform_function(self):
        from qualang_tools.config.waveform_tools import drag_cosine_pulse_waveforms

        I, Q = drag_cosine_pulse_waveforms(
            amplitude=self.amplitude,
            length=self.length,
            alpha=self.alpha,
            anharmonicity=self.anharmonicity,
            detuning=self.detuning,
        )
        I, Q = np.array(I), np.array(Q)

        I_rot = I * np.cos(self.axis_angle) - Q * np.sin(self.axis_angle)
        Q_rot = I * np.sin(self.axis_angle) + Q * np.cos(self.axis_angle)

        return I_rot + 1.0j * Q_rot

DragGaussianPulse

Bases: Pulse

Gaussian-based DRAG pulse that compensate for the leakage and AC stark shift.

These DRAG waveforms has been implemented following the next Refs.: Chen et al. PRL, 116, 020501 (2016) https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.020501 and Chen's thesis https://web.physics.ucsb.edu/~martinisgroup/theses/Chen2018.pdf

Parameters:

Name	Type	Description	Default
length	int	The pulse length in ns.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required
amplitude	float	The amplitude in volts.	required
sigma	float	The gaussian standard deviation.	required
alpha	float	The DRAG coefficient.	required
anharmonicity	float	f_21 - f_10 - The differences in energy between the 2-1 and the 1-0 energy levels, in Hz.	required
detuning	float	The frequency shift to correct for AC stark shift, in Hz.	required
subtracted	bool	If true, returns a subtracted Gaussian, such that the first and last points will be at 0 volts. This reduces high-frequency components due to the initial and final points offset. Default is true.	required

Source code in quam/components/pulses.py

@quam_dataclass
class DragGaussianPulse(Pulse):
    """Gaussian-based DRAG pulse that compensate for the leakage and AC stark shift.

    These DRAG waveforms has been implemented following the next Refs.:
    Chen et al. PRL, 116, 020501 (2016)
    https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.020501
    and Chen's thesis
    https://web.physics.ucsb.edu/~martinisgroup/theses/Chen2018.pdf

    Args:
        length (int): The pulse length in ns.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
        amplitude (float): The amplitude in volts.
        sigma (float): The gaussian standard deviation.
        alpha (float): The DRAG coefficient.
        anharmonicity (float): f_21 - f_10 - The differences in energy between the 2-1
            and the 1-0 energy levels, in Hz.
        detuning (float): The frequency shift to correct for AC stark shift, in Hz.
        subtracted (bool): If true, returns a subtracted Gaussian, such that the first
            and last points will be at 0 volts. This reduces high-frequency components
            due to the initial and final points offset. Default is true.

    """

    axis_angle: float
    amplitude: float
    sigma: float
    alpha: float
    anharmonicity: float
    detuning: float = 0.0
    subtracted: bool = True

    def __post_init__(self) -> None:
        return super().__post_init__()

    def waveform_function(self):
        from qualang_tools.config.waveform_tools import drag_gaussian_pulse_waveforms

        I, Q = drag_gaussian_pulse_waveforms(
            amplitude=self.amplitude,
            length=self.length,
            sigma=self.sigma,
            alpha=self.alpha,
            anharmonicity=self.anharmonicity,
            detuning=self.detuning,
            subtracted=self.subtracted,
        )
        I, Q = np.array(I), np.array(Q)

        I_rot = I * np.cos(self.axis_angle) - Q * np.sin(self.axis_angle)
        Q_rot = I * np.sin(self.axis_angle) + Q * np.cos(self.axis_angle)

        return I_rot + 1.0j * Q_rot

FlatTopGaussianPulse

Bases: Pulse

Gaussian pulse with flat top QuAM component.

Parameters:

Name	Type	Description	Default
length	int	The total length of the pulse in samples.	required
amplitude	float	The amplitude of the pulse in volts.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required
flat_length	int	The length of the pulse's flat top in samples. The rise and fall lengths are calculated from the total length and the flat length.	required

Source code in quam/components/pulses.py

@quam_dataclass
class FlatTopGaussianPulse(Pulse):
    """Gaussian pulse with flat top QuAM component.

    Args:
        length (int): The total length of the pulse in samples.
        amplitude (float): The amplitude of the pulse in volts.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
        flat_length (int): The length of the pulse's flat top in samples.
            The rise and fall lengths are calculated from the total length and the
            flat length.
    """

    amplitude: float
    axis_angle: float = None
    flat_length: int

    def waveform_function(self):
        from qualang_tools.config.waveform_tools import flattop_gaussian_waveform

        rise_fall_length = (self.length - self.flat_length) // 2
        if not self.flat_length + 2 * rise_fall_length == self.length:
            raise ValueError(
                "FlatTopGaussianPulse rise_fall_length (=length-flat_length) must be"
                f" a multiple of 2 ({self.length} - {self.flat_length} ="
                f" {self.length - self.flat_length})"
            )

        waveform = flattop_gaussian_waveform(
            amplitude=self.amplitude,
            flat_length=self.flat_length,
            rise_fall_length=rise_fall_length,
            return_part="all",
        )
        waveform = np.array(waveform)

        if self.axis_angle is not None:
            waveform = waveform * np.exp(1j * self.axis_angle)

        return waveform

GaussianPulse

Bases: Pulse

Gaussian pulse QuAM component.

Parameters:

Name	Type	Description	Default
amplitude	float	The amplitude of the pulse in volts.	required
length	int	The length of the pulse in samples.	required
sigma	float	The standard deviation of the gaussian pulse. Should generally be less than half the length of the pulse.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required
subtracted	bool	If true, returns a subtracted Gaussian, such that the first and last points will be at 0 volts. This reduces high-frequency components due to the initial and final points offset. Default is true.	required

Source code in quam/components/pulses.py

@quam_dataclass
class GaussianPulse(Pulse):
    """Gaussian pulse QuAM component.

    Args:
        amplitude (float): The amplitude of the pulse in volts.
        length (int): The length of the pulse in samples.
        sigma (float): The standard deviation of the gaussian pulse.
            Should generally be less than half the length of the pulse.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
        subtracted (bool): If true, returns a subtracted Gaussian, such that the first
            and last points will be at 0 volts. This reduces high-frequency components
            due to the initial and final points offset. Default is true.
    """

    amplitude: float
    length: int
    sigma: float
    axis_angle: float = None
    subtracted: bool = True

    def waveform_function(self):
        t = np.arange(self.length, dtype=int)
        center = (self.length - 1) / 2
        waveform = self.amplitude * np.exp(-((t - center) ** 2) / (2 * self.sigma**2))

        if self.subtracted:
            waveform = waveform - waveform[-1]

        if self.axis_angle is not None:
            waveform = waveform * np.exp(1j * self.axis_angle)

        return waveform

Pulse

Bases: QuamComponent

QuAM base component for a pulse.

Pulses are added to a channel using

channel.operations["pulse_name"] = pulse

The Pulse class is an abstract base class, and should not be instantiated directly. Instead, use one of the subclasses such as: - ConstantReadoutPulse - DragPulse - SquarePulse - GaussianPulse or create a custom subclass. In this case, the method waveform_function should be implemented.

Parameters:

Name	Type	Description	Default
operation	str	The operation of the pulse, either "control" or "measurement". Default is "control".	required
length	int	The length of the pulse in samples.	required
digital_marker	(str, list)	The digital marker to use for the pulse. Can be a string, in which case it is a reference to a digital marker in the config, or a list of tuples of (sample, length) pairs. Default is None.	required

Note

The unique pulse label is automatically generated from the channel name and the pulse name, the same for the waveform and digital marker names. The pulse label is defined as "{channel_name}.{pulse_name}.pulse". The waveform label is defined as "{channel_name}.{pulse_name}.wf". The digital marker label is defined as "{channel_name}.{pulse_name}.dm".

Source code in quam/components/pulses.py

@quam_dataclass
class Pulse(QuamComponent):
    """QuAM base component for a pulse.

    Pulses are added to a channel using
    ```
    channel.operations["pulse_name"] = pulse
    ```

    The `Pulse` class is an abstract base class, and should not be instantiated
    directly. Instead, use one of the subclasses such as:
    - `ConstantReadoutPulse`
    - `DragPulse`
    - `SquarePulse`
    - `GaussianPulse`
    or create a custom subclass. In this case, the method `waveform_function` should
    be implemented.

    Args:
        operation (str): The operation of the pulse, either "control" or "measurement".
            Default is "control".
        length (int): The length of the pulse in samples.
        digital_marker (str, list, optional): The digital marker to use for the pulse.
            Can be a string, in which case it is a reference to a digital marker in the
            config, or a list of tuples of (sample, length) pairs. Default is None.

    Note:
        The unique pulse label is automatically generated from the channel name and
        the pulse name, the same for the waveform and digital marker names.
        The pulse label is defined as `"{channel_name}.{pulse_name}.pulse"`.
        The waveform label is defined as `"{channel_name}.{pulse_name}.wf"`.
        The digital marker label is defined as `"{channel_name}.{pulse_name}.dm"`.

    """

    operation: ClassVar[str] = "control"
    length: int
    id: str = None

    digital_marker: Union[str, List[Tuple[int, int]]] = None

    @property
    def channel(self):
        """The channel to which the pulse is attached, None if no channel is attached"""
        from quam.components.channels import Channel

        if isinstance(self.parent, Channel):
            return self.parent
        elif hasattr(self.parent, "parent") and isinstance(self.parent.parent, Channel):
            return self.parent.parent
        else:
            return None

    @property
    def name(self):
        if self.channel is None:
            raise AttributeError(
                f"Cannot get full name of pulse '{self}' because it is not"
                " attached to a channel"
            )

        if self.id is not None:
            name = self.id
        else:
            name = self.parent.get_attr_name(self)

        return f"{self.channel.name}{str_ref.DELIMITER}{name}"

    @property
    def pulse_name(self):
        return f"{self.name}{str_ref.DELIMITER}pulse"

    @property
    def waveform_name(self):
        return f"{self.name}{str_ref.DELIMITER}wf"

    @property
    def digital_marker_name(self):
        return f"{self.name}{str_ref.DELIMITER}dm"

    def calculate_waveform(self) -> Union[float, complex, List[float], List[complex]]:
        """Calculate the waveform of the pulse.

        This function calls `Pulse.waveform_function`, which should generally be
        subclassed, to generate the waveform.

        This function then processes the results such that IQ waveforms are cast
        into complex values.

        Returns:
            The processed waveform, which can be either
            - a single float for a constant single-channel waveform,
            - a single complex number for a constant IQ waveform,
            - a list of floats for an arbitrary single-channel waveform,
            - a list of complex numbers for an arbitrary IQ waveform,
        """
        waveform = self.waveform_function()

        # Optionally convert IQ waveforms to complex waveform
        if isinstance(waveform, tuple) and len(waveform) == 2:
            if isinstance(waveform[0], (list, np.ndarray)):
                waveform = np.array(waveform[0]) + 1.0j * np.array(waveform[1])
            else:
                waveform = waveform[0] + 1.0j * waveform[1]

        return waveform

    def waveform_function(
        self,
    ) -> Union[
        float,
        complex,
        List[float],
        List[complex],
        Tuple[float, float],
        Tuple[List[float], List[float]],
    ]:
        """Function that returns the waveform of the pulse.

        The waveform function should use the relevant parameters from the pulse, which
        is passed as the only argument.

        This function is called from `Pulse.calculate_waveform`

        Returns:
            The waveform of the pulse. Can be one of the following:
            - a single float for a constant single-channel waveform,
            - a single complex number for a constant IQ waveform,
            - a list of floats for an arbitrary single-channel waveform,
            - a list of complex numbers for an arbitrary IQ waveform,
            - a tuple of floats or float lists for an arbitrary IQ waveform
        """
        ...

    def _config_add_pulse(self, config: Dict[str, Any]):
        """Add the pulse to the config

        The config entry is added to `config["pulses"][self.pulse_name]`
        """
        assert self.operation in ["control", "measurement"]
        assert isinstance(self.length, int)

        pulse_config = config["pulses"][self.pulse_name] = {
            "operation": self.operation,
            "length": self.length,
        }
        if self.digital_marker is not None:
            pulse_config["digital_marker"] = self.digital_marker

    def _config_add_waveforms(self, config):
        """Add the waveform to the config

        For a single waveform, the config entry is added to
        `config["waveforms"]["{channel_name}.{pulse_name}.wf"]`.
        For an IQ waveform, two config entries are added to
        `config["waveforms"]["{channel_name}.{pulse_name}.wf.I"]` and with suffix `Q`.

        Raises:
            ValueError: If the waveform type (single or IQ) does not match the parent
                channel type (SingleChannel, IQChannel, InOutIQChannel, MWChannel,
                InOutMWChannel).
        """

        from quam.components.channels import SingleChannel, IQChannel, MWChannel

        pulse_config = config["pulses"][self.pulse_name]

        waveform = self.calculate_waveform()
        if waveform is None:
            return

        pulse_config["waveforms"] = {}

        if isinstance(waveform, numbers.Number):
            wf_type = "constant"
            if isinstance(waveform, complex):
                waveforms = {"I": waveform.real, "Q": waveform.imag}
            elif isinstance(self.channel, (IQChannel, MWChannel)):
                waveforms = {"I": waveform, "Q": 0.0}
            else:
                waveforms = {"single": waveform}

        elif isinstance(waveform, (list, np.ndarray)):
            wf_type = "arbitrary"
            if np.iscomplexobj(waveform):
                waveforms = {"I": list(waveform.real), "Q": list(waveform.imag)}
            elif isinstance(self.channel, (IQChannel, MWChannel)):
                waveforms = {"I": waveform, "Q": np.zeros_like(waveform)}
            else:
                waveforms = {"single": list(waveform)}
        else:
            raise ValueError("unsupported return type")

        # Add check that waveform type (single or IQ) matches parent
        if "single" in waveforms and not isinstance(self.channel, SingleChannel):
            raise ValueError(
                "Waveform type 'single' not allowed for (IQChannel, MWChannel)"
                f" '{self.channel.name}'"
            )
        elif "I" in waveforms and not isinstance(self.channel, (IQChannel, MWChannel)):
            raise ValueError(
                "Waveform type 'IQ' not allowed for SingleChannel"
                f" '{self.channel.name}'"
            )

        for suffix, waveform in waveforms.items():
            waveform_name = self.waveform_name
            if suffix != "single":
                waveform_name += f"{str_ref.DELIMITER}{suffix}"

            sample_label = "sample" if wf_type == "constant" else "samples"

            config["waveforms"][waveform_name] = {
                "type": wf_type,
                sample_label: waveform,
            }
            pulse_config["waveforms"][suffix] = waveform_name

    def _config_add_digital_markers(self, config):
        """Add the digital marker to the config

        The config entry is added to
        `config["digital_waveforms"]["{channel_name}.{pulse_name}.dm"]` and also
        registered in
        `config["pulses"]["{channel_name}.{pulse_name}.pulse"]["digital_marker"]`.

        If the digital marker is a string, it is assumed to be a reference to a
        digital marker already defined in the config.
        """
        if isinstance(self.digital_marker, str):
            # Use a common config digital marker
            if self.digital_marker not in config["digital_waveforms"]:
                raise KeyError(
                    "{self.name}.digital_marker={self.digital_marker} not in"
                    " config['digital_waveforms']"
                )
            digital_marker_name = self.digital_marker
        else:
            config["digital_waveforms"][self.digital_marker_name] = {
                "samples": self.digital_marker
            }
            digital_marker_name = self.digital_marker_name

        config["pulses"][self.pulse_name]["digital_marker"] = digital_marker_name

    def apply_to_config(self, config: dict) -> None:
        """Adds this pulse, waveform, and digital marker to the QUA configuration.

        See [`QuamComponent.apply_to_config`][quam.core.quam_classes.QuamComponent.apply_to_config]
        for details.
        """
        if self.channel is None:
            return

        self._config_add_pulse(config)
        self._config_add_waveforms(config)

        if self.digital_marker:
            self._config_add_digital_markers(config)

channel property

The channel to which the pulse is attached, None if no channel is attached

apply_to_config(config)

Adds this pulse, waveform, and digital marker to the QUA configuration.

See QuamComponent.apply_to_config for details.

Source code in quam/components/pulses.py

def apply_to_config(self, config: dict) -> None:
    """Adds this pulse, waveform, and digital marker to the QUA configuration.

    See [`QuamComponent.apply_to_config`][quam.core.quam_classes.QuamComponent.apply_to_config]
    for details.
    """
    if self.channel is None:
        return

    self._config_add_pulse(config)
    self._config_add_waveforms(config)

    if self.digital_marker:
        self._config_add_digital_markers(config)

calculate_waveform()

Calculate the waveform of the pulse.

This function calls Pulse.waveform_function, which should generally be subclassed, to generate the waveform.

This function then processes the results such that IQ waveforms are cast into complex values.

Returns:

Type	Description
Union[float, complex, List[float], List[complex]]	The processed waveform, which can be either
Union[float, complex, List[float], List[complex]]	a single float for a constant single-channel waveform,
Union[float, complex, List[float], List[complex]]	a single complex number for a constant IQ waveform,
Union[float, complex, List[float], List[complex]]	a list of floats for an arbitrary single-channel waveform,
Union[float, complex, List[float], List[complex]]	a list of complex numbers for an arbitrary IQ waveform,

Source code in quam/components/pulses.py

def calculate_waveform(self) -> Union[float, complex, List[float], List[complex]]:
    """Calculate the waveform of the pulse.

    This function calls `Pulse.waveform_function`, which should generally be
    subclassed, to generate the waveform.

    This function then processes the results such that IQ waveforms are cast
    into complex values.

    Returns:
        The processed waveform, which can be either
        - a single float for a constant single-channel waveform,
        - a single complex number for a constant IQ waveform,
        - a list of floats for an arbitrary single-channel waveform,
        - a list of complex numbers for an arbitrary IQ waveform,
    """
    waveform = self.waveform_function()

    # Optionally convert IQ waveforms to complex waveform
    if isinstance(waveform, tuple) and len(waveform) == 2:
        if isinstance(waveform[0], (list, np.ndarray)):
            waveform = np.array(waveform[0]) + 1.0j * np.array(waveform[1])
        else:
            waveform = waveform[0] + 1.0j * waveform[1]

    return waveform

waveform_function()

Function that returns the waveform of the pulse.

The waveform function should use the relevant parameters from the pulse, which is passed as the only argument.

This function is called from Pulse.calculate_waveform

Returns:

Type	Description
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	The waveform of the pulse. Can be one of the following:
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	a single float for a constant single-channel waveform,
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	a single complex number for a constant IQ waveform,
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	a list of floats for an arbitrary single-channel waveform,
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	a list of complex numbers for an arbitrary IQ waveform,
Union[float, complex, List[float], List[complex], Tuple[float, float], Tuple[List[float], List[float]]]	a tuple of floats or float lists for an arbitrary IQ waveform

Source code in quam/components/pulses.py

def waveform_function(
    self,
) -> Union[
    float,
    complex,
    List[float],
    List[complex],
    Tuple[float, float],
    Tuple[List[float], List[float]],
]:
    """Function that returns the waveform of the pulse.

    The waveform function should use the relevant parameters from the pulse, which
    is passed as the only argument.

    This function is called from `Pulse.calculate_waveform`

    Returns:
        The waveform of the pulse. Can be one of the following:
        - a single float for a constant single-channel waveform,
        - a single complex number for a constant IQ waveform,
        - a list of floats for an arbitrary single-channel waveform,
        - a list of complex numbers for an arbitrary IQ waveform,
        - a tuple of floats or float lists for an arbitrary IQ waveform
    """
    ...

ReadoutPulse

Bases: BaseReadoutPulse, ABC

QuAM abstract base component for most readout pulses.

This class is a subclass of ReadoutPulse and should be used for most readout pulses. It provides a default implementation of the integration_weights_function method, which is suitable for most cases.

Parameters:

Name	Type	Description	Default
length	int	The length of the pulse in samples.	required
digital_marker	(str, list)	The digital marker to use for the pulse. Default is "ON".	required
integration_weights	(list[float], list[tuple[float, int]])	The integration weights, can be either - a list of floats (one per sample), the length must match the pulse length - a list of tuples of (weight, length) pairs, the sum of the lengths must match the pulse length	required
integration_weights_angle	float	The rotation angle for the integration weights in radians.	required

Source code in quam/components/pulses.py

@quam_dataclass
class ReadoutPulse(BaseReadoutPulse, ABC):
    """QuAM abstract base component for most readout pulses.

    This class is a subclass of `ReadoutPulse` and should be used for most readout
    pulses. It provides a default implementation of the `integration_weights_function`
    method, which is suitable for most cases.

    Args:
        length (int): The length of the pulse in samples.
        digital_marker (str, list, optional): The digital marker to use for the pulse.
            Default is "ON".
        integration_weights (list[float], list[tuple[float, int]], optional): The
            integration weights, can be either
            - a list of floats (one per sample), the length must match the pulse length
            - a list of tuples of (weight, length) pairs, the sum of the lengths must
              match the pulse length
        integration_weights_angle (float, optional): The rotation angle for the
            integration weights in radians.
    """

    integration_weights: Union[List[float], List[Tuple[float, int]]] = None
    integration_weights_angle: float = 0

    def integration_weights_function(self) -> List[Tuple[Union[complex, float], int]]:
        from qualang_tools.config import convert_integration_weights

        phase = np.exp(1j * self.integration_weights_angle)

        if self.integration_weights is None or not len(self.integration_weights):
            integration_weights = [(1, self.length)]
        elif isinstance(self.integration_weights[0], float):
            integration_weights = convert_integration_weights(self.integration_weights)
        else:
            integration_weights = self.integration_weights

        return {
            "real": [(phase.real * w, l) for w, l in integration_weights],
            "imag": [(phase.imag * w, l) for w, l in integration_weights],
            "minus_real": [(-phase.real * w, l) for w, l in integration_weights],
            "minus_imag": [(-phase.imag * w, l) for w, l in integration_weights],
        }

SquarePulse

Bases: Pulse

Square pulse QuAM component.

Parameters:

Name	Type	Description	Default
length	int	The length of the pulse in samples.	required
digital_marker	(str, list)	The digital marker to use for the pulse.	required
amplitude	float	The amplitude of the pulse in volts.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required

Source code in quam/components/pulses.py

@quam_dataclass
class SquarePulse(Pulse):
    """Square pulse QuAM component.

    Args:
        length (int): The length of the pulse in samples.
        digital_marker (str, list, optional): The digital marker to use for the pulse.
        amplitude (float): The amplitude of the pulse in volts.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
    """

    amplitude: float
    axis_angle: float = None

    def waveform_function(self):
        waveform = self.amplitude

        if self.axis_angle is not None:
            waveform = waveform * np.exp(1j * self.axis_angle)
        return waveform

SquareReadoutPulse

Bases: ReadoutPulse, SquarePulse

QuAM component for a square readout pulse.

Parameters:

Name	Type	Description	Default
length	int	The length of the pulse in samples.	required
digital_marker	(str, list)	The digital marker to use for the pulse. Default is "ON".	required
amplitude	float	The constant amplitude of the pulse.	required
axis_angle	float	IQ axis angle of the output pulse in radians. If None (default), the pulse is meant for a single channel or the I port of an IQ channel If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).	required
integration_weights	(list[float], list[tuple[float, int]])	The integration weights, can be either - a list of floats (one per sample), the length must match the pulse length - a list of tuples of (weight, length) pairs, the sum of the lengths must match the pulse length	required
integration_weights_angle	float	The rotation angle for the integration weights in radians.	required

Source code in quam/components/pulses.py

@quam_dataclass
class SquareReadoutPulse(ReadoutPulse, SquarePulse):
    """QuAM component for a square readout pulse.

    Args:
        length (int): The length of the pulse in samples.
        digital_marker (str, list, optional): The digital marker to use for the pulse.
            Default is "ON".
        amplitude (float): The constant amplitude of the pulse.
        axis_angle (float, optional): IQ axis angle of the output pulse in radians.
            If None (default), the pulse is meant for a single channel or the I port
                of an IQ channel
            If not None, the pulse is meant for an IQ channel (0 is X, pi/2 is Y).
        integration_weights (list[float], list[tuple[float, int]], optional): The
            integration weights, can be either
            - a list of floats (one per sample), the length must match the pulse length
            - a list of tuples of (weight, length) pairs, the sum of the lengths must
              match the pulse length
        integration_weights_angle (float, optional): The rotation angle for the
            integration weights in radians.
    """

    ...

WaveformPulse

Bases: Pulse

Pulse that uses a pre-defined waveform, as opposed to a function.

For a single channel, only waveform_I is required. For an IQ channel, both waveform_I and waveform_Q are required.

The length of the pulse is derived from the length of waveform_I.

Parameters:

Name	Type	Description	Default
waveform_I	list[float]	The in-phase waveform.	required
waveform_Q	list[float]	The quadrature waveform.	required

Source code in quam/components/pulses.py

@quam_dataclass
class WaveformPulse(Pulse):
    """Pulse that uses a pre-defined waveform, as opposed to a function.

    For a single channel, only `waveform_I` is required.
    For an IQ channel, both `waveform_I` and `waveform_Q` are required.

    The length of the pulse is derived from the length of `waveform_I`.

    Args:
        waveform_I (list[float]): The in-phase waveform.
        waveform_Q (list[float], optional): The quadrature waveform.
    """

    waveform_I: List[float]  # pyright: ignore
    waveform_Q: Optional[List[float]] = None
    # Length is derived from the waveform_I length, but still needs to be declared
    # to satisfy the dataclass, but we'll override its behavior
    length: Optional[int] = None  # pyright: ignore

    @property
    def length(self):  # noqa: 811
        if not isinstance(self.waveform_I, Iterable):
            return None
        return len(self.waveform_I)

    @length.setter
    def length(self, length: Optional[int]):
        if length is not None and not isinstance(length, property):
            raise AttributeError(f"length is not writable with value {length}")

    def waveform_function(self):
        if self.waveform_Q is None:
            return np.array(self.waveform_I)
        return np.array(self.waveform_I) + 1.0j * np.array(self.waveform_Q)

    def to_dict(
        self, follow_references: bool = False, include_defaults: bool = False
    ) -> Dict[str, Any]:
        d = super().to_dict(follow_references, include_defaults)
        d.pop("length")
        return d