Generating OpenQASM 3 + OpenPulse in Python
Project description
OQpy: Generating OpenQASM 3 + OpenPulse in Python
The goal of oqpy
("ock-pie") is to make it easy to generate OpenQASM 3 + OpenPulse in Python. The
oqpy
library builds off of the openqasm3
and openpulse
packages,
which serve as Python reference implementations of the abstract syntax tree (AST) for the
OpenQASM 3 and OpenPulse grammars.
What are OpenQASM 3 and OpenPulse?
OpenQASM is an imperative programming language designed for near-term quantum computing algorithms
and applications. OpenQASM 3 extends the original specification by adding support
for classical logic, explicit timing, and pulse-level definitions. The latter is enabled via the use
of calibration grammars which allow quantum hardware builders to extend the language
to support hardware-specific directives via cal
and defcal
blocks. One such grammar is
OpenPulse, which provides the instructions required for pulse-based control of
many common quantum computing architectures (e.g. superconducting qubits).
Installation and Getting Started
OQpy can be installed from PyPI or from source in an environment with Python 3.7 or greater.
To install it from PyPI (via pip
), do the following:
pip install oqpy
To instead install OQpy from source, do the following from within the repository after cloning it:
poetry install
Next, check out the following example to get a sense of the kinds of programs we can write with OQpy.
Example: Ramsey Interferometry
A common and useful experiment for qubit characterization is Ramsey interferometry, which can be used for two purposes: performing a careful measurement of a qubit’s resonant frequency, and for investigating how long a qubit retains its coherence. In a typical Ramsey experiment, one varies the length of a delay between the two π/2 pulses, and then measures the state of the qubit. Below, we'll create a Ramsey interferometry experiment in OpenQASM 3 using OQpy. As part of this, we’ll use the OpenPulse grammar to allow this experiment to specify its operation implementations at the calibrated pulse level.
import oqpy
prog = oqpy.Program() # create a new oqpy program
# Declare frames: transmon driving frame and readout receive/transmit frames
xy_frame = oqpy.FrameVar(oqpy.PortVar("dac0"), 6.431e9, name="xy_frame")
rx_frame = oqpy.FrameVar(oqpy.PortVar("adc0"), 5.752e9, name="rx_frame")
tx_frame = oqpy.FrameVar(oqpy.PortVar("dac1"), 5.752e9, name="tx_frame")
# Declare the type of waveform we are working with
constant_waveform = oqpy.declare_waveform_generator(
"constant",
[("length", oqpy.duration),
("amplitude", oqpy.float64)],
)
gaussian_waveform = oqpy.declare_waveform_generator(
"gaussian",
[("length", oqpy.duration),
("sigma", oqpy.duration),
("amplitude", oqpy.float64)],
)
# Provide gate / operation definitions as defcals
qubit = oqpy.PhysicalQubits[1] # get physical qubit 1
with oqpy.defcal(prog, qubit, "reset"):
prog.delay(1e-3) # reset to ground state by waiting 1 ms
with oqpy.defcal(prog, qubit, "measure"):
prog.play(tx_frame, constant_waveform(2.4e-6, 0.2))
prog.capture(rx_frame, constant_waveform(2.4e-6, 1))
with oqpy.defcal(prog, qubit, "x90"):
prog.play(xy_frame, gaussian_waveform(32e-9, 8e-9, 0.2063))
# Loop over shots (i.e. repetitions)
delay_time = oqpy.DurationVar(0, "delay_time") # initialize a duration
with oqpy.ForIn(prog, range(100), "shot_index"):
prog.set(delay_time, 0) # reset delay time to zero
# Loop over delays
with oqpy.ForIn(prog, range(101), "delay_index"):
(prog.reset(qubit) # prepare in ground state
.gate(qubit, "x90") # pi/2 pulse (90° rotation about the x-axis)
.delay(delay_time, qubit) # variable delay
.gate(qubit, "x90") # pi/2 pulse (90° rotation about the x-axis)
.measure(qubit) # final measurement
.increment(delay_time, 100e-9)) # increase delay by 100 ns
Running print(prog.to_qasm(encal_declarations=True))
generates the following OpenQASM:
OPENQASM 3.0;
defcalgrammar "openpulse";
cal {
extern constant(duration, float[64]) -> waveform;
extern gaussian(duration, duration, float[64]) -> waveform;
port dac1;
port adc0;
port dac0;
frame tx_frame = newframe(dac1, 5752000000.0, 0);
frame rx_frame = newframe(adc0, 5752000000.0, 0);
frame xy_frame = newframe(dac0, 6431000000.0, 0);
}
duration delay_time = 0.0ns;
defcal reset $1 {
delay[1000000.0ns];
}
defcal measure $1 {
play(tx_frame, constant(2400.0ns, 0.2));
capture(rx_frame, constant(2400.0ns, 1));
}
defcal x90 $1 {
play(xy_frame, gaussian(32.0ns, 8.0ns, 0.2063));
}
for int shot_index in [0:99] {
delay_time = 0.0ns;
for int delay_index in [0:100] {
reset $1;
x90 $1;
delay[delay_time] $1;
x90 $1;
measure $1;
delay_time += 100.0ns;
}
}
Contributing
We welcome contributions to OQpy including bug fixes, feature requests, etc. To get started, check
out our contributing guidelines. Those who make a nontrivial contribution to the
source code will be added as an author to the CITATION.cff
file (see below).
Citation
If you use OQpy in your work or research, please cite it using the metadata in the
CITATION.cff
file in the repository (which includes a
Zenodo DOI). You can copy the citation in BibTeX format using the
"Cite this repository" widget in the About section of the
repository page on GitHub.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file oqpy-0.3.0.tar.gz
.
File metadata
- Download URL: oqpy-0.3.0.tar.gz
- Upload date:
- Size: 28.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.4.2 CPython/3.11.3 Darwin/21.6.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e4acbdd8a72b5f7d1ed9a8cb422c1cefea0e802f4dc6154df64d1179913c69c1 |
|
MD5 | ece293ce96e4f8ebe9306d9067f39e34 |
|
BLAKE2b-256 | 8ef5734bc01dd5f55e4857959db87d35a10071b9053bb9a40adfcc3fbb0a9231 |
File details
Details for the file oqpy-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: oqpy-0.3.0-py3-none-any.whl
- Upload date:
- Size: 33.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.4.2 CPython/3.11.3 Darwin/21.6.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | efec9ff9f3a9bff61699aab85a997ef0e539327f8599af0dbd3eb0fbeefcde74 |
|
MD5 | 5f7de75caee207106b91bc2e27f11a3a |
|
BLAKE2b-256 | cb3df71b450bc3813a66737cd2c491be4e5599006301040bcc4ff95cd41e7a98 |