envelopes-qc

High performance tools for building and decoding envelopes used in superconducting qubit experiments.

Build

Before building on Linux, install the libcerf3 and libcerf-dev packages in the src/libcerf folder, or download and install them according to your Linux distribution.

python -m build -w -v

Installation

pip install envelopes-qc

Or build from source:

  1. Clone the repository to your machine.
  2. Install the package locally with one of the following commands:
# Development build (compile in place)
python setup.py build_ext --inplace

# Install into the active Python environment
pip install .

Usage

from envelopes import evc
import numpy as np

# Create a GaussianDRAG envelope
center = 15
width = 10
pi_pulse = evc.GaussianDRAG(
    t0=center, w=width, amp=1.0, coef=0.5, df=0.1, phase=0.0
)

# Schedule multiple pulses
amp = np.array([1, 0.3, 0.7, 0.8])
dt = np.array([0, 50, 100, 200])
xy = evc.align(pi_pulse, dt, amp)  # align the envelope to the given dt and amplitude

# The following code has equivalent effect but is less efficient:
# xy = 0
# for _dt, _amp in zip(dt, amp):
#     xy += (pi_pulse >> _dt) * _amp

# Decode the envelope
resolution = 0.5
wc = evc.WaveCache(resolution)
# t, wave = evc.decode_envelope(xy, wc)  # default start and end from the envelope
t_start, wave = evc.decode_envelope(xy, wc, start=-50, end=250)
# wave is already a zero-copy numpy ndarray view of the internal buffer
t_list = np.arange(t_start, t_start + len(wave) * resolution, resolution)

import matplotlib.pyplot as plt

plt.figure()
plt.plot(t_list, wave.real, '.-', label='real')
plt.plot(t_list, wave.imag, '.-', label='imag')
plt.legend()
plt.xlabel('time')
plt.ylabel('amplitude')
plt.tight_layout()
plt.show()

Supported Envelopes

See tests/supported_envelopes.ipynb and the detailed envelope reference in doc/envelope_reference.md.

Speed Test

See tests/speed_test.ipynb.

Documentation

Detailed usage documentation is available in the doc/ folder. Start from doc/index.md:

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.1.5)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for xsb from gravatar.com xsb

Credits

Author: Shibo Xu

Requires

Python >=3.9

Classifiers

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