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Running on Helmi

Running Jobs

Jobs can be submitted to the q_fiqci queue by specifying --partition=q_fiqci in batch scripts.

Helmi currently supports submitting jobs using Qiskit or Cirq. Qiskit and Cirq scripts can only be submitted as ordinary python files. To submit and run jobs on Helmi you need to use the correct environment on LUMI.

  • First, run module use /appl/local/quantum/modulefiles. The available modules will now show up on module avail.
  • Second, depending on if you want to use the Qiskit or Cirq environment, run:
    • module load helmi_qiskit or
    • module load helmi_cirq

helmi_qiskit and helmi_cirq provide pre-made python environments to directly run on Helmi. If you wish to add your own python packages to the pre-made python environment you can do so with python -m pip install --user package.

Creating your own python environment

Users can create their own python environment if they wish. The only prerequisite is to load the helmi_standard module. To create your own environment the container wrapper tool is recommended.

The current support software versions on helmi are:

Cirq on IQM     cirq_iqm    >= 11.9, < 12.0  # helmi_cirq has 11.9
Qiskit on IQM   qiskit_iqm  >= 8.3, < 9.0  # helmi_qiskit has 9.0
IQM client  iqm_client  >= 12.5, < 13.0

Here is an example batch script to submit jobs on Helmi

#!/bin/bash

#SBATCH --job-name=helmijob   # Job name
#SBATCH --account=project_<id>  # Project for billing
#SBATCH --partition=q_fiqci   # Partition (queue) name
#SBATCH --ntasks=1              # One task (process)
#SBATCH --cpus-per-task=1     # Number of cores (threads)
#SBATCH --time=00:15:00         # Run time (hh:mm:ss)

module use /appl/local/quantum/modulefiles

# uncomment correct line:
# module load helmi_qiskit
# or
# module load helmi_cirq

python your_python_script.py

The batch script can then be submitted with sbatch. You can also submit interactive jobs through srun. 

srun --account=project_<id> -t 00:15:00 -c 1 -n 1 --partition q_fiqci python your_python_script.py

The helmi_* module sets up the correct python environment to use Qiskit or Cirq in conjunction with Helmi.

Qiskit

To load the Qiskit module use module load helmi_qiskit.

In Qiskit python scripts you will need to include the following:

import os
from qiskit_iqm import IQMProvider

HELMI_CORTEX_URL = os.getenv('HELMI_CORTEX_URL')  # This is set when loading the module

provider = IQMProvider(HELMI_CORTEX_URL)
backend = provider.get_backend()

shots = 1000  # Set the number of shots you wish to run with

# Create your quantum circuit.

job = execute(circuit, backend, shots=shots)  # execute your quantum circuit
counts = job.result().get_counts()
print(counts)

Cirq

To load the Cirq module use module load helmi_cirq.

import os
from cirq_iqm.iqm_sampler import IQMSampler

HELMI_CORTEX_URL = os.getenv('HELMI_CORTEX_URL')  # This is set when loading the module

sampler = IQMSampler(HELMI_CORTEX_URL)

shots = 1000

result = sampler.run(circuit, repetitions=shots)

Additional examples

An additional set of examples can be found here. The examples emphasize the difference between running on a simulator and a real physical quantum computer, and how to construct your circuits for optimum results on Helmi. The repository also contains some useful scripts for submitting jobs.

Simulated test runs

As quantum resources can be scarce, it is recommended that you prepare the codes and algorithms you intend to run on Helmi in advance. To help with this process, qiskit-on-iqm provides a fake noise model backend. You can run the fake noise model backend locally on your laptop for simulation and testing.

A set of Qiskit and Cirq examples and scripts for guidance in using the LUMI-Helmi partition are also available. You can find these here.

Job Metadata

The figures of merit (or quality metrics set) may be necessary for publishing work produced on Helmi. It also gives an idea as to the current status of Helmi. In helmi-examples there is a helper script to get the calibration data including the figures of merit. The script can be found here. Note that querying the latest calibration data may given an incomplete set of figures of merit. Therefore calibration set IDs should be saved along with Job IDs.

Additional metadata about your job can be queried directly with Qiskit. For example:

provider = IQMProvider(HELMI_CORTEX_URL)
backend = provider.get_backend()

#Retrieving backend information
print(f'Native operations: {backend.operation_names}')
print(f'Number of qubits: {backend.num_qubits}')
print(f'Coupling map: {backend.coupling_map}')

job = execute(circuit, backend, shots=shots)
result = job.result()
exp_result = result._get_experiment(circuit)

print("Job ID: ", job.job_id())  # Retrieving the submitted job id
print(result.request.circuits)  # Retrieving the circuit request sent
print("Calibration Set ID: ", exp_result.calibration_set_id)  # Retrieving the current calibration set id. 
print(result.request.qubit_mapping)  # Retrieving the qubit mapping
print(result.request.shots)  # Retrieving the number of requested shots. 

# retrieve a job using the job_id from a previous session
#old_job = backend.retrieve_job(job_id)

Save your Job ID!

Note that there is currently no method to list previous Job ID's therefore it is recommended to always print your Job ID after job submission and save it somewhere! The same applies for the calibration set id.

For further information on the figures of merit contact the CSC Service Desk, reachable at servicedesk@csc.fi.

Last update: July 11, 2023