Execution

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Usage

• IDE
• SDK

When viewing a quantum program in the “Quantum Program” page, after synthesizing your model or uploading your quantum program file, click “Execute”:In the next screen you can choose your execution preferences and run your quantum program.

from classiq import *


# Design your quantum model
@qfunc
def main(res: Output[QBit]) -> None:
    allocate(res)


# Synthesize a quantum program from the quantum model
quantum_program = synthesize(main)

# Execute the quantum program and access the result
ex = execute(quantum_program)
results = ex.result()

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sample Function

from classiq import synthesize
from classiq.execution import sample

quantum_program = synthesize(main)

# Single execution — returns a DataFrame
df = sample(quantum_program, backend="simulator", num_shots=1000)

from classiq.execution import sample

params_batch = [
    {"theta": 0.1},
    {"theta": 0.5},
    {"theta": 1.0},
]

# Batch execution — returns a list of DataFrames, one per parameter set
results = sample(quantum_program, parameters=params_batch)

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Sampling OpenQASM

from qiskit import QuantumCircuit, qasm2

from classiq.execution import sample

qc = QuantumCircuit(1)
qc.h(0)
openqasm = qasm2.dumps(qc)

df = sample(openqasm, backend="simulator", num_shots=500)

from classiq.execution import sample

openqasm = """
OPENQASM 3;
include "stdgates.inc";
qubit[1] q;
h q[0];
"""

df = sample(openqasm, backend="simulator", num_shots=500)

• Do not pass parameters for OpenQASM strings; use a QuantumProgram if you need Qmod main parameters, or express parameters inside the QASM (e.g. Qiskit Parameter and assign_parameters before dumping).
• Batch sampling with a list of parameter dicts is only defined for QuantumProgram execution.

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Execution Preferences

• Backend preferences, such as provider, backend name, and credentials. See Cloud Providers.
• Cost estimation: Estimate execution cost before running, for any supported provider.
• Number of shots to use.
• Job name to use.
• Transpilation options. You can set the transpilation level (and whether or not to transpile) in the Classiq executor by setting the transpile_to_hardware field (shown as the “Transpilation Option” field in the IDE execution page). For more information on the transpilation levels, see quantum program transpilation.

• IDE
• SDK

Choose your backend preferences in the “Execute Quantum Circuit” window:You can select more than one backend on which to run, but note that a maximum of five backends can be selected at a time.Optionally configure more execution preferences in the “Execution Configuration” window:Finally, execute your program by clicking “Run”.

You can set your execution preferences in the quantum model before the synthesis, as in this example:

from classiq import *

# Define execution preferences
execution_preferences = ExecutionPreferences(
    num_shots=1000
)  # set your real preferences instead!

# Set the execution preferences
with ExecutionSession(qprog, execution_preferences=execution_preferences) as es:
    results_more_shots = es.sample()

If not specified, the default backend is Classiq’s simulator, which doesn’t require any provider credentials.

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Jobs

from classiq import get_execution_jobs

jobs = get_execution_jobs()

from classiq import ExecutionJob

job = ExecutionJob.from_id("00000000-0000-0000-0000-000000000000")

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Results

• IDE
• SDK

The IDE shows a visualized view of each result returned from execution.The most common result type is the measurements of your quantum program:It is possible to filter the results by specifying them:

Once you have an ExecutionJob object (from the execute function or from querying previous jobs), you can retrieve its result using the result() method, which takes an optional argument timeout_sec specifying the polling timeout in seconds (0 means to poll once and return).The execution job result contains all the saved results from the execution process in a list. Each result is a SavedResult object with these fields:

• name
• value: The result object.
• value_type: The result type, which is one of the values of the SavedResultValueType enum.

Results can be inspected and manipulated in a pandas DataFrame by running:

res = job.get_sample_result()
df = res.dataframe

For sample jobs, you can retrieve the actual circuits that were submitted to the provider (after transpilation and parameter assignment) using get_submitted_circuits(). Each returned circuit supports to_qasm() and to_qiskit():

circuits = job.get_submitted_circuits()
for circuit in circuits:
    qasm_str = circuit.to_qasm()
    qc = circuit.to_qiskit()  # Qiskit QuantumCircuit

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Cancellation