qiskit-documentation/docs/guides/serverless-port-code.mdx

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---
title: Port code to Qiskit Serverless
description: How to port existing code to leverage Qiskit Serverless
---
# Port code to Qiskit Serverless
The following example demonstrates how to port existing code to leverage Qiskit Serverless.
## Update the experiment
<Tabs>
<TabItem value="LocalExperiment" label="Local Experiment">
```python
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit.circuit.random import random_circuit
qc_random = [(random_circuit(20, 20, measure=True)) for _ in range(30)]
optimization_level = 3
# Get backend
service = QiskitRuntimeService(channel="ibm_quantum", token=API_TOKEN)
backend = service.get_backend(backend_name)
pass_manager = generate_preset_pass_manager(
optimization_level=optimization_level, backend=backend
)
# @distribute_task(target={"cpu": 1})
def transpile_parallel(circuit, pass_manager):
"""Distributed transpilation for an abstract circuit into an ISA circuit for a given backend."""
isa_circuit = pass_manager.run(circuit)
return isa_circuit
transpiled_circuits = [
transpile_parallel(circuit, pass_manager)
for circuit in circuits
]
print(transpiled_circuits)
```
</TabItem>
<TabItem value="Serverless" label="Serverless">
```python
# transpile_remote.py
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_serverless import get_arguments, save_result, distribute_task, get
from qiskit_ibm_runtime import QiskitRuntimeService
# Get program arguments
arguments = get_arguments()
circuits = arguments.get("circuits")
backend_name = arguments.get("backend_name")
optimization_level = arguments.get("optimization_level")
pass_manager = generate_preset_pass_manager(
optimization_level=optimization_level, backend=backend_name
)
# Distribute task across workers
@distribute_task(target={"cpu": 1})
def transpile_parallel(circuit, pass_manager):
"""Distributed transpilation for an abstract circuit into an ISA circuit for a given backend."""
isa_circuit = pass_manager.run(circuit)
return isa_circuit
try:
# Get backend
service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.get_backend(backend_name)
# run distributed tasks as async function
# we get task references as a return type
sample_task_references = [
transpile_parallel(circuit, pass_manager)
for circuit in circuits
]
# now we need to collect results from task references
results = get(sample_task_references)
# Return results
save_result({
"transpiled_circuits": results
})
except Exception as e:
# Exception handling
import traceback
print(traceback.format_exc())
```
</TabItem>
</Tabs>
## Upload to Qiskit Serverless
```python
from qiskit_ibm_catalog import QiskitServerless, QiskitFunction
# Authenticate to the remote cluster and submit the pattern for remote execution
serverless = QiskitServerless(
token=API_TOKEN
)
transpile_remote_demo = QiskitFunction(
title="transpile_remote_serverless",
entrypoint="transpile_remote.py",
working_dir="./source_files/",
)
serverless.upload(transpile_remote_demo)
```
Output
```text
'transpile_remote_serverless'
```
## Remotely run in Qiskit Serverless
```python
from qiskit.circuit.random import random_circuit
from qiskit_ibm_runtime import QiskitRuntimeService
# Setup inputs
qc_random = [(random_circuit(20, 20, measure=True)) for _ in range(30)]
backend = "ibm_brisbane"
optimization_level = 3
# Running program
transpile_remote_serverless = serverless.get('transpile_remote_serverless')
job = transpile_remote_serverless.run(
circuits=qc_random,
backend=backend,
optimization_level=optimization_level
)
job.job_id()
```
Output
```text
'727e921d-512d-4b7d-af97-fe29e93ce7ea'
```
## Next steps
<Admonition type="info" title="Recommendations">
- Read a paper in which researchers used Qiskit Serverless and quantum-centric supercomputing to [explore quantum chemistry](https://arxiv.org/abs/2405.05068v1).
</Admonition>