cQASM converter

As usual, we start by importing the needed libraries. Note that this notebook requires the installation of cQASM (which can be easily done with pip install libqasm). Perceval-interop can also be installed with the command: pip install perceval-interop[cqasm] to automatically install cQASM.

from perceval_interop import CQASMConverter
from perceval import pdisplay, SimulatedComputer, ExecutionFactory

Then we write our cQASM program.

cqasm_program = """
version 3
qubit[2] q
H q[0]
CNOT q[0], q[1]
"""

We convert it with the Perceval CQASMConverter.

perceval_experiment = CQASMConverter().convert(cqasm_program, use_postselection=False)

The output probabilities can now be computed using Perceval.

computer = SimulatedComputer("SLOS")
with computer.acquire():
    r = ExecutionFactory(computer, perceval_experiment).probs()['results']
print('results:', r)
pdisplay(perceval_experiment)

results: {
        |1,0,1,0>: 0.5000000000000002
        |0,1,0,1>: 0.4999999999999999
}

The 1.0 version of cQASM is also supported, as shown in the example below.

# version 1.0
cqasm_program = f"""
version 1.0

# a basic cQASM example
qubits 2

.prepare
prep_z q[0:1]

.entangle
H q[0]
CNOT q[0], q[1]

.measurement
measure_all
"""

perceval_experiment = CQASMConverter().convert(cqasm_program, use_postselection=False)

with computer.acquire():
    r = ExecutionFactory(computer, perceval_experiment).probs()['results']
print('results:', r)
pdisplay(perceval_experiment)

results: {
        |1,0,1,0>: 0.5000000000000002
        |0,1,0,1>: 0.4999999999999999
}

The cQASM program can also be retrieved from a file by using CQASMConverter().convert(path_to_cqasm_program).