waveforms#
Contains function to generate most basic waveforms.
These functions are intended to be used to generate waveforms defined in the
pulse_library
.
Examples of waveforms that are too advanced are flux pulses that require knowledge of
the flux sensitivity and interaction strengths and qubit frequencies.
Module Contents#
Functions#

Generate a square pulse. 

Generate a square pulse with imaginary amplitude. 

Generate a ramp pulse. 

Ramps from zero to a finite value in discrete steps. 

A softened square pulse. 

Produces a linear chirp signal. 

Generates a DRAG pulse consisting of a Gaussian \(G\) as the I and a 

Generates the sudden net zero waveform from Negîrneac et al. [NAM+21]. 

Wrapper function around 

Rotate a wave in the complex plane. 

Generates a skewed hermite pulse for single qubit rotations in NV centers. 
 square(t: numpy.ndarray  List[float], amp: float  complex) numpy.ndarray [source]#
Generate a square pulse.
 square_imaginary(t: numpy.ndarray  List[float], amp: float  complex) numpy.ndarray [source]#
Generate a square pulse with imaginary amplitude.
 ramp(t, amp, offset=0) numpy.ndarray [source]#
Generate a ramp pulse.
 staircase(t: numpy.ndarray  List[float], start_amp: float  complex, final_amp: float  complex, num_steps: int) numpy.ndarray [source]#
Ramps from zero to a finite value in discrete steps.
 Parameters:
t – Times at which to evaluate the function.
start_amp – Starting amplitude.
final_amp – Final amplitude to reach on the last step.
num_steps – Number of steps to reach final value.
 Returns:
The real valued waveform.
 soft_square(t, amp)[source]#
A softened square pulse.
 Parameters:
t – Times at which to evaluate the function.
amp – Amplitude of the pulse.
 chirp(t: numpy.ndarray, amp: float, start_freq: float, end_freq: float) numpy.ndarray [source]#
Produces a linear chirp signal.
The frequency is determined according to the relation:
The waveform is produced simply by multiplying with a complex exponential.
 Parameters:
t – Times at which to evaluate the function.
amp – Amplitude of the envelope.
start_freq – Start frequency of the Chirp.
end_freq – End frequency of the Chirp.
 Returns:
The complex waveform.
 drag(t: numpy.ndarray, G_amp: float, D_amp: float, duration: float, nr_sigma: int = 3, phase: float = 0, subtract_offset: str = 'average') numpy.ndarray [source]#
Generates a DRAG pulse consisting of a Gaussian \(G\) as the I and a Derivative \(D\) as the Qcomponent (Motzoi et al. [MGRW09] and Gambetta et al. [GMMW11]).
All inputs are in s and Hz. phases are in degree.
\(G(t) = G_{amp} e^{(t\mu)^2/(2\sigma^2)}\).
\(D(t) = D_{amp} \frac{(t\mu)}{\sigma} G(t)\).
 Parameters:
t – Times at which to evaluate the function.
G_amp – Amplitude of the Gaussian envelope.
D_amp – Amplitude of the derivative component, the DRAGpulse parameter.
duration – Duration of the pulse in seconds.
nr_sigma – After how many sigma the Gaussian is cut off.
phase – Phase of the pulse in degrees.
subtract_offset –
Instruction on how to subtract the offset in order to avoid jumps in the waveform due to the cutoff.
’average’: subtract the average of the first and last point.
’first’: subtract the value of the waveform at the first sample.
’last’: subtract the value of the waveform at the last sample.
’none’, None: don’t subtract any offset.
 Returns:
complex waveform
 sudden_net_zero(t: numpy.ndarray, amp_A: float, amp_B: float, net_zero_A_scale: float, t_pulse: float, t_phi: float, t_integral_correction: float)[source]#
Generates the sudden net zero waveform from Negîrneac et al. [NAM+21].
The waveform consists of a square pulse with a duration of half
t_pulse
and an amplitude ofamp_A
, followed by an idling period (0 V) with durationt_phi
, followed again by a square pulse with amplitudeamp_A * net_zero_A_scale
and a duration of halft_pulse
, followed by a integral correction period with durationt_integral_correction
.The last sample of the first pulse has amplitude
amp_A * amp_B
. The first sample of the second pulse has amplitudeamp_A * net_zero_A_scale * amp_B
.The amplitude of the integral correction period is such that
sum(waveform) == 0
.If the total duration of the pulse parts is less than the duration set by the
t
array, the remaining samples will be set to 0 V.The various pulse part durations are rounded down (floor) to the sample rate of the
t
array. Sincet_pulse
is the total duration of the two square pulses, half this duration is rounded to the sample rate. For example:import numpy as np from quantify_scheduler.waveforms import sudden_net_zero t = np.linspace(0, 9e9, 10) # 1 GSa/s amp_A = 1.0 amp_B = 0.5 net_zero_A_scale = 0.8 t_pulse = 5.0e9 # will be rounded to 2 pulses of 2 ns t_phi = 2.6e9 # rounded to 2 ns t_integral_correction = 4.4e9 # rounded to 4 ns sudden_net_zero( t, amp_A, amp_B, net_zero_A_scale, t_pulse, t_phi, t_integral_correction )
array([ 1. , 0.5 , 0. , 0. , 0.4 , 0.8 , 0.075, 0.075, 0.075, 0.075])
 Parameters:
t – A uniformly sampled array of times at which to evaluate the function.
amp_A – Amplitude of the main square pulse
amp_B – Scaling correction for the final sample of the first square and first sample of the second square pulse.
net_zero_A_scale – Amplitude scaling correction factor of the negative arm of the netzero pulse.
t_pulse – The total duration of the two half square pulses. The duration of each half is rounded to the sample rate of the
t
array.t_phi – The idling duration between the two half pulses. The duration is rounded to the sample rate of the
t
array.t_integral_correction – The duration in which any nonzero pulse amplitude needs to be corrected. The duration is rounded to the sample rate of the
t
array.
 interpolated_complex_waveform(t: numpy.ndarray, samples: numpy.ndarray, t_samples: numpy.ndarray, interpolation: str = 'linear', **kwargs) numpy.ndarray [source]#
Wrapper function around
scipy.interpolate.interp1d
, which takes the array of (complex) samples, interpolates the real and imaginary parts separately and returns the interpolated values at the specified times. Parameters:
t – Times at which to evaluated the to be returned waveform.
samples – An array of (possibly complex) values specifying the shape of the waveform.
t_samples – An array of values specifying the corresponding times at which the
samples
are evaluated.interpolation – The interpolation method to use, by default “linear”.
kwargs – Optional keyword arguments to pass to
scipy.interpolate.interp1d
.
 Returns:
An array containing the interpolated values.
 rotate_wave(wave: numpy.ndarray, phase: float) numpy.ndarray [source]#
Rotate a wave in the complex plane.
 Parameters:
wave – Complex waveform, real component corresponds to I, imaginary component to Q.
phase – Rotation angle in degrees.
 Returns:
Rotated complex waveform.
 skewed_hermite(t: numpy.ndarray, duration: float, amplitude: float, skewness: float, phase: float, pi2_pulse: bool = False, center: float  None = None, duration_over_char_time: float = 6.0) numpy.ndarray [source]#
Generates a skewed hermite pulse for single qubit rotations in NV centers.
A Hermite pulse is a Gaussian multiplied by a second degree Hermite polynomial. See Beukers [Beu19], Appendix A.2.
The skew parameter is a first order amplitude correction to the hermite pulse. It increases the fidelity of the performed gates. See Beukers [Beu19], section 4.2. To get a “standard” hermite pulse, use
skewness=0
.The hermite factors are taken from equation 44 and 45 of Warren [War84].
 Parameters:
t – Times at which to evaluate the function.
duration – Duration of the pulse in seconds.
amplitude – Amplitude of the pulse.
skewness – Skewness in the frequency space
phase – Phase of the pulse in degrees.
pi2_pulse – if True, the pulse will be pi/2 otherwise pi pulse
center – Optional: time after which the pulse center occurs. If
None
, it is automatically set to duration/2.duration_over_char_time – Ratio of the pulse duration and the characteristic time of the hermite polynomial. Increasing this number will compress the pulse. By default, 6.
 Returns:
complex skewed waveform