Running your first quantum computing job on Helmi through LUMI

If you've applied for a project, been accepted, setup your ssh keys and gained access to LUMI, then the next step is to run your first quantum computing job on a real quantum computer! This is a guide for exactly how to do that. The only thing you need to know is your project number!

Configuring the environment

The first step after you have logged into LUMI (via ssh lumi on your terminal) is to configure the environment. The base environment when first logging into LUMI does not provide the necessary tools to submit quantum jobs, therefore a quantum software stack has been created which sets up the correct python virtual environments and the correct environment variables. This is accessed through the LMOD system on LUMI using modules.

To use the quantum software stack you first need to load the Quantum module tree.

module use /appl/local/quantum/modulefiles

Alternatively, you can achieve the same result by loading the Local-quantum module.

module load Local-quantum

You can then see the list of available modules with module avail. The quantum modules should be at the top! In this walkthrough Qiskit will be used, therefore the next step is to load the module into our current environment with

module load helmi_qiskit

Creating your first quantum program

The next step is to create your quantum circuit! Here a simple bell state will be created between two qubits, demonstrating entanglement between them! For this we will be using Qiskit but the steps are very similar for Cirq. It is good practice to work in your projects scratch directory, which you can navigate to with cd /scratch/project_xxx, inserting your project number.

Tip!

You can quickly see your LUMI workspaces with module load lumi-workspaces and lumi-workspaces

Let us first create our python file with nano first_quantum_job.py. Here we use nano but if you are comfortable you can also use vim or emacs. This will bring up the nano text editor, the useful commands are at the bottom, to save and exit CTRL-X + Y.

Importing the libraries

First let's import the right python libraries

import os
from qiskit import QuantumCircuit, QuantumRegister, transpile
from iqm.qiskit_iqm import IQMProvider

Creating the circuit

The quantum circuit is created by defining a QuantumRegister which hold our qubits and classical bits respectively. As this circuit only requires 2 qubits we only create a QuantumRegister of size 2. The number of shots is also defined here. The number of shots is the number of times a quantum circuit is executed. We do this because quantum computers are probabilistic machines and by repeating the experiment many times we can get close to a deterministic result to be able to draw conclusions from. A good number of shots for your first quantum job is shots = 1000. Increasing the shots will increase the precision of your results.

shots = 1000  # Number of repetitions of the Quantum Circuit

qreg = QuantumRegister(2, "qB")
circuit = QuantumCircuit(qreg, name='Bell pair circuit')

Now we actually add some gates to the circuit. Here a Hadamard gate is added to the first qubit or the first qubit in the quantum register. Then a controlled-x gate is added with two arguments as it is a two qubit gate.

circuit.h(qreg[0])  # Hadamard gate on the first qubit in the Quantum Register
circuit.cx(qreg[1], qreg[0])  # Controlled-X gate between the second qubit and first qubit
circuit.measure_all()  # Measure all qubits in the Quantum Register.

Note that measure_all() creates it's own ClassicalRegister!

Now the circuit is created! If you wish you can see what your circuit looks like by adding a print statement print(circuit.draw()) and quickly running the python script.

Setting the backend

First we need to set our provider and backend. The provider is the service which gives an interface to the quantum computer and the backend provides the tools necessary to submitting the quantum job. The HELMI_CORTEX_URL is the endpoint to reach Helmi and is only reachable inside the q_fiqci partition. This environment variable is set automatically when loading any of the helmi_* modules such as the helmi_qiskit module.

HELMI_CORTEX_URL = os.getenv('HELMI_CORTEX_URL')

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

Decomposing the circuit (Optional)

The next step is optional and where the quantum circuit into you've just created into it's basis gates. These basis gates are the actual quantum gates on the quantum computer. The process of decomposition involves turning the above Hadamard and controlled-x gates into something that can be physically run on the quantum computer. Helmi's basis gates are the entangling gate controlled-z and the one-qubit phased-rx gate. In Qiskit these are defined in the backend and can be printed with backend.operation_names.

circuit_decomposed = transpile(circuit, backend=backend)

You can also print your circuit like before with print(circuit_decomposed.draw()) to see what it looks like!

Optional Qubit Mapping

This is an optional step but may be useful to extracting the best out of the quantum computer. This is a python dictionary which simply states which qubits in the Quantum register should be mapped to which physical qubit.

qubit_mapping = {
                qreg[0]: 0,
                qreg[1]: 2,
            }

Here we are mapping the first qubit in the quantum register to the first of Helmi's qubits, QB1, located at the zeroth location due to Qiskit's use of zero-indexing. The second qubit is then mapped to QB3. This is where we have made use of Helmi's topology.

The two qubit Controlled-X gate we implemented in our circuit is currently on the second of our two qubits in the Quantum register, qreg[1]. Due to Helmi's topology this needs to be mapped to QB3 on Helmi. The 1 qubit Hadamard gate can be mapped to any of the outer qubits, QB1, QB2, QB4, QB5, here we choose QB1.

Note that this step is entirely optional. Using the execute function automatically does the mapping based on the information stored in the backend. Inputting the qubit mapping simply gives more control to the user.

To transpile a circuit using the specified qubit mapping you can do the following:

circuit_decomposed = transpile(circuit, backend=backend, initial_layout=qubit_mapping)

Submitting the job

Now we can run our quantum job!

job = backend.run(circuit_decomposed, shots=shots)

Viewing the status of your job

You can view the status of your job with

job.status()

Results

Before submitting we need to ensure we can get some results! The quantum job will return what are called counts. Counts are the accumulation of results from the 1000 times the circuit is submitted to the QPU. Each time the circuit is submitted a binary state is returned, this is then added to the tally. In this case as we are submitting a 2 qubit circuit there are 4 possible resulting states: 00, 11, 01, 10. The expected results should be that approximately 50% of the counts should be in state 00 and 50% in state 11. The states of the qubits are thus entangled: if one of the qubits is measured to be in state |0>, the other one will immediately also collapse to the same state, and vice versa. As real quantum computers are not perfect, you will most likely also see that some measurements find the states |01> and |10>.

To print your results add:

counts = job.result().get_counts()
print(counts)

You can also print the entirety of job.result() which will contain all the information about your jobs results.

Save your file

Once you've made your first quantum program remember to save! CTRL+X then Y to save your file.

Running the job through LUMI

To run your quantum programme on LUMI you will need to submit the job through the SLURM batch scheduler on LUMI. Accessing Helmi is done through the q_fiqci partition. In the same directory where you have saved your quantum program, you can submit the job to SLURM using:

srun --account project_xxx -t 00:15:00 -c 1 -n 1 --partition q_fiqci python -u first_quantum_job.py

Remember to add your own project account!

This submits the job interactively meaning that the output will be printed straight to the terminal screen. If you wish you can also submit it using sbatch using this skeleton batch script. Using nano as before create the script batch_script.sh.

#!/bin/bash -l

#SBATCH --job-name=helmijob   # Job name
#SBATCH --output=helmijob.o%j # Name of stdout output file
#SBATCH --error=helmijob.e%j  # Name of stderr error file
#SBATCH --partition=q_fiqci   # Partition (queue) name
#SBATCH --ntasks=1              # One task (process)
#SBATCH --mem-per-cpu=2G       # memory allocation
#SBATCH --cpus-per-task=1     # Number of cores (threads)
#SBATCH --time=00:15:00         # Run time (hh:mm:ss)
#SBATCH --account=project_xxx  # Project for billing

module use /appl/local/quantum/modulefiles
module load helmi_qiskit

python -u first_quantum_job.py

This can be submitted with sbatch batch_script.sh in the same directory as your python file. Jobs in the SLURM queue can be monitored through squeue -u username and after the job has completed your results can be found in the helmijob.oxxxxx file. This can be printed to the terminal with cat.

Congratulations!

Congratulations! You have just run your first job on Helmi.

The full python script can be found below.

import os

from qiskit import QuantumCircuit, QuantumRegister, transpile
from iqm.qiskit_iqm import IQMProvider

shots = 1000

qreg = QuantumRegister(2, "QB")
circuit = QuantumCircuit(qreg, name='Bell pair circuit')

circuit.h(qreg[0])
circuit.cx(qreg[0], qreg[1])
circuit.measure_all()

# Uncomment if you wish to print the circuit
# print(circuit.draw())

HELMI_CORTEX_URL = os.getenv('HELMI_CORTEX_URL')

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}')

transpiled_circuit = transpile(circuit, backend)
job = backend.run(transpiled_circuit, shots=shots)
result = job.result()
exp_result = job.result()._get_experiment(circuit)
# You can retrieve the job at a later date with backend.retrieve_job(job_id)
# Uncomment the following lines to get more information about your submitted job
# print("Job ID: ", job.job_id())
# print(result.request.circuits)
# print("Calibration Set ID: ", exp_result.calibration_set_id)
# print(result.request.qubit_mapping)
# print(result.request.shots)

counts = result.get_counts()
print(counts)

Do you find this page useful?

Yes

Thank you for your feedback!

No

Thank you! Your feedback is appreciated.

Please let us know how we could improve this page by contacting us or by creating an issue in GitHub. You can also edit this page yourself and contribute your improvements by creating a pull request.