Manage Qiskit Serverless compute and data resources

Qiskit Serverless allows you to manage compute and data across your Qiskit pattern, including CPUs, QPUs, and other compute accelerators.

Parallel workflows

For classical tasks that can be parallelized, use the @distribute_task decorater to define compute requirements needed to perform a task. Start by recalling the transpile_parallel.py example from the Write your first Qiskit Serverless program topic:

# /source_files/transpile_remote.py
 
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_serverless import distribute_task
 
@distribute_task(target={"cpu": 1})
def transpile_remote(circuit, optimization_level, backend):
    """Transpiles an abstract circuit into an ISA circuit for a given backend."""
    pass_manager = generate_preset_pass_manager(
        optimization_level=optimization_level,
		backend=backend
    )
    isa_circuit = pass_manager.run(circuit)
    return isa_circuit

In this example, you decorated the transpile_remote() method with @distribute_task(target={"cpu": 1}). When run, this creates an asynchronous parallel worker task with a single CPU core, and returns with a reference to track the worker. To fetch the result, pass the reference to the get() method:

from qiskit_serverless import get
 
transpile_worker_reference = transpile_remote(circuit, optimization_level, backend)
result = get(transpile_worker_reference)

You can also create and run multiple parallel tasks as follows:

transpile_worker_references = [
    transpile_remote(circuit, optimization_level, backend)
    for circuit in circuit_list
]
 
results = get(transpile_worker_references)

Explore different task configurations

You can flexibly allocate CPU, GPU, and memory for your tasks via @distribute_task(). For Qiskit Serverless on IBM Quantum™ Platform, each program is equipped with 16 CPU cores and 32 GB RAM, which can be allocated dynamically as needed.

CPU cores can be allocated as full CPU cores, or even fractional allocations, as shown in the following.

Memory is allocated in number of bytes. Recall that there are 1024 bytes in a kilobyte, 1024 kilobytes in a megabyte, and 1024 megabytes in a gigabyte. To allocate 2 GB of memory for your worker, you need to allocate "mem": 2 * 1024 * 1024 * 1024.

@distribute_task(target={
    "cpu": 16,
    "mem": 32 * 1024 * 1024 * 1024
})
def transpile_remote(circuit, optimization_level, backend):
    return None

Automatic QPU selection

This example demonstrates how to use IBMQPUSelector to automate the process of selecting which qubits to use from a set of available QPUs. Instead of manually selecting a QPU, Qiskit Serverless automatically allocates a QPU according to desired criteria. IBMQPUSelectors are imported from server-side qiskit_serverless_tools.selectors modules, and must be invoked from your uploaded program.

Here, IBMLeastNoisyQPUSelector finds the QPU that yields the least-noisy qubit subgraph for the input circuit, from among the QPUs available to you through your IBM Quantum account.

For each IBMQPUSelector, the context is set in the constructor. All IBMQPUSelectors require Qiskit Runtime credentials. The IBMLeastNoisyQPUSelector requires a circuit and transpile options specifying how the circuit should be optimized for each QPU, to determine the most optimal QPU and qubit layout.

All IBMQPUSelectors implement a get_backend method, which retrieves the optimal QPU with respect to the given context. The get_backend method also allows for additional filtering of the QPUs. It is implemented using the same interface as the QiskitRuntimeService.backends method.

# source_files/transpile_parallel.py
 
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit.circuit.random import random_circuit
from qiskit_serverless_tools.selectors import IBMLeastNoisyQPUSelector
 
service = QiskitRuntimeService(channel='ibm_quantum', token=API_TOKEN)
 
abstract_circuit = random_circuit(
    num_qubits=100, depth=4, measure=True
)
 
selector = IBMLeastNoisyQPUSelector(
    service, circuit=abstract_circuit, transpile_options={"optimization_level": 3}
)
backend = selector.get_backend(min_num_qubits=127)
target_circuit = selector.optimized_circuit

You can also use the IBMLeastBusyQPUSelector to find a QPU that can support the circuit width but with the shortest queue.

# source_files/transpile_parallel.py
 
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_serverless_tools.selectors import IBMLeastBusyQPUSelector
 
backend = IBMLeastBusyQPUSelector(service).get_backend(min_num_qubits=127)
pm = generate_preset_pass_manager(optimization_level=3, backend=backend)
target_circuit = pm.run(abstract_circuit)

Manage data across your program

Qiskit Serverless allows you to manage files in the /data directory across all your programs. This includes several limitations:

• Only tar and h5 files are supported today
• This is only a flat /data storage, and cannot have /data/folder/ subdirectories

The following shows how to upload files. Be sure you have authenticated to Qiskit Serverless with your IBM Quantum account (see Deploy to IBM Quantum Platform for instructions).

import tarfile
 
# Create a tar
filename = "transpile_demo.tar"
file = tarfile.open(filename,"w")
file.add("source_files/transpile_remote.py")
file.close()
 
# Upload the tar to Serverless data directory
serverless.file_upload(filename)

Output:

'{"message":"/usr/src/app/media/5f37582aa306c50013fac285/transpile_demo.tar"}'

Next, you can list all the files in your data directory. This data is accessible to all programs.

serverless.files()

Output:

['transpile_demo.tar']

This can be done from a program by using file_download() to download the file to the program environment, and uncompressing the tar.

# transpile_parallel.py
 
import tarfile
 
files = serverless.files()
demo_file = files[0]
downloaded_tar = serverless.file_download(demo_file)
 
print(downloaded_tar)
with tarfile.open(downloaded_tar, 'r') as tar:
    tar.extractall()

Output:

100%|██████████| 10.2k/10.2k [00:00<00:00, 10.0MiB/s]

downloaded_3d3c4bf6_transpile_demo.tar

At this point, your program can interact with the files, as you would a local experiment. file_upload() , file_download(), and file_delete() can be called from your local experiment, or your uploaded program, for consistent and flexible data management.

Next steps