Python Model Editor: Creating QMOD models using Python

The classiq python package allows you to create QMOD models using Python. With classiq you can write a collection of Python functions that will be translated to native QMOD function definitions, and subsequently can be used to define a QMOD model.

The following code snippet demonstrates the usage:

SDK

from math import pi
from classiq import QFunc, QParam, OutputQVar, QVar, PHASE, allocate
from classiq import create_model, synthesize, show


@QFunc
def foo(r: QParam[float], qv: QVar[1]) -> None:
    PHASE(theta=r * pi, target=qv)


@QFunc
def main(res: OutputQVar[1]) -> None:
    allocate(1, res)
    foo(r=1, qv=res)
    foo(r=0.5, qv=res)


model = create_model(main)
qprog = synthesize(model)
show(qprog)

Note that main outputs a single-qubit quantum variable to be sampled during execution. The variable qv is declared using OutputQVar[1] and allocated using the builtin function allocate. foo is then called twice, each time applying a phase on the state. Running the snippet above results in the following circuit:

In the rest of this section we'll explore various additional capabilities classiq allows when creating models.