User guide
Contents
User guide#
This guide illustrates the main features of Qiskit on IQM. You are encouraged to run the demonstrated code snippets and check the output yourself.
Note
At the moment IQM does not provide a quantum computing service open to the general public. Please contact our sales team to set up your access to an IQM quantum computer.
Hello, world!#
Here’s the quickest and easiest way to execute a small computation on an IQM quantum computer and check that things are set up correctly:
Download the bell_measure.py example file (Save Page As…)
Install Qiskit on IQM as instructed below (feel free to skip the import statement)
Install Cortex CLI and log in as instructed in the documentation
Set the environment variable as instructed by Cortex CLI after logging in
Run
$ python bell_measure.py --cortex_server_url https://demo.qc.iqm.fi/cocos– replace the example URL with the correct oneIf you’re connecting to a real quantum computer, the output should show almost half of the measurements resulting in ‘00’ and almost half in ‘11’ – if this is the case, things are set up correctly!
Installation#
The recommended way is to install the distribution package qiskit-iqm directly from the
Python Package Index (PyPI):
$ pip install qiskit-iqm
After installation Qiskit on IQM can be imported in your Python code as follows:
from iqm import qiskit_iqm
Running a quantum circuit on an IQM quantum computer#
In this section we demonstrate the practicalities of using Qiskit on IQM on an example of constructing and executing a simple quantum circuit on an IQM quantum computer.
Let’s consider the following quantum circuit which prepares and measures a GHZ state:
from qiskit import QuantumCircuit
qc = QuantumCircuit(3, 3)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.measure_all()
print(qc.draw(output='text'))
┌───┐ ░ ┌─┐
q_0: ┤ H ├──■────■───░─┤M├──────
└───┘┌─┴─┐ │ ░ └╥┘┌─┐
q_1: ─────┤ X ├──┼───░──╫─┤M├───
└───┘┌─┴─┐ ░ ║ └╥┘┌─┐
q_2: ──────────┤ X ├─░──╫──╫─┤M├
└───┘ ░ ║ ║ └╥┘
meas_0: ═══════════════════╩══╬══╬═
║ ║
meas_1: ══════════════════════╩══╬═
║
meas_2: ═════════════════════════╩═
To execute this circuit on an IQM quantum computer you need to initialize an IQMProvider instance
with the IQM server URL, use it to retrieve an IQMBackend instance representing the
quantum computer, and use Qiskit’s execute function as usual:
from qiskit import execute
from iqm.qiskit_iqm import IQMProvider
provider = IQMProvider(iqm_server_url)
backend = provider.get_backend()
job = execute(qc, backend, shots=1000)
print(job.result().get_counts())
Note that the code snippet above assumes that you have set the variable iqm_server_url.
You can optionally set IQM backend specific options as additional keyword arguments to the execute method (which
passes the values down to IQMBackend.run()). For example, if you know an ID of a specific calibration set that
you want to use, you can provide it as follows:
job = execute(circuit, backend, shots=1000, calibration_set_id="f7d9642e-b0ca-4f2d-af2a-30195bd7a76d")
Alternatively, you can update the values of the options directly on the backend instance using the IQMBackend.set_options()
and then call execution methods without specifying additional keyword arguments. You can view all available options and
their current values using backend.options. Below table summarizes currently available options:
Name |
Description |
|---|---|
shots |
Type: Number of shots. |
calibration_set_id |
Type: Indicates the calibration set to use. Defaults to None, which means the IQM server will use the best available calibration set automatically. |
circuit_duration_check |
Type: Enable or disable server-side circuit duration checks. The default value is True, which means if any job is estimated to take unreasonably long compared to the coherence times of the qubits, or too long in wall-clock time, the server will reject it. This option can be used to disable this behaviour. In normal use, the circuit duration check should always remain enabled. |
heralding_mode |
Type: Heralding mode to use during execution. The default value is “none”. |
circuit_callback |
Type: A function that accepts a list of |
If the IQM server you are connecting to requires authentication, you will also have to use
Cortex CLI to retrieve and automatically refresh access tokens,
then set the IQM_TOKENS_FILE environment variable, as instructed, to point to the tokens file.
See Cortex CLI’s documentation for details.
Alternatively, you may authenticate yourself using the IQM_AUTH_SERVER,
IQM_AUTH_USERNAME and IQM_AUTH_PASSWORD environment variables, or pass them as
arguments to IQMProvider.__init__(), however this approach is less secure and considered deprecated.
The results of a job, that was executed with IQM quantum computer, contain the original request with the qubit mapping that was used in execution. You can check this mapping once execution has finished.
print(job.result().request.qubit_mapping)
[
SingleQubitMapping(logical_name='0', physical_name='QB1'),
SingleQubitMapping(logical_name='1', physical_name='QB2'),
SingleQubitMapping(logical_name='2', physical_name='QB3')
]
The job result also contains metadata of the execution including timestamps of the various steps of processing the
job. The timestamps are stored in dict timestamps in the metadata. The dict contains a timestamp for starting
and ending of each step. The timestamps are stored with keys describing the point of processing where they were
stored. For example, the timestamp of starting the circuit compilation is stored with key compile_start.
In the same way the other steps have their own timestamps with keys consisting of the step name and a _start or
_end suffix. In addition to processing step timestamps, there are also timestamps for the job itself,
job_start for when the job request was received by the server and job_end for when the job processing
was finished.
The job processing has three steps, compile where the circuits are converted to pulse schedules, submit
where the pulse schedules are submitted for execution and execution where the pulse schedules are executed
and the measurement results are returned.
If the processing of the job is terminated before it is complete, for example due to an error, the timestamps of processing steps that were not taken are not present in the dict.
The timestamps dict can be accessed as timestamps attribute of the job results.
For example:
print(job.result().timestamps['job_start'])
print(job.result().timestamps['compile_start'])
print(job.result().timestamps['execution_end'])
The backend instance we created above provides all the standard backend functionality that one expects from a
backend in Qiskit. For this example, I am connected to an IQM backend that features a 5-qubit chip with star-like
connectivity:
QB1
|
QB2 - QB3 - QB4
|
QB5
Let’s examine its basis gates and the coupling map through the backend instance
print(f'Native operations of the backend: {backend.operation_names}')
print(f'Coupling map of the backend: {backend.coupling_map}')
Native operations of the backend: ['id', 'r', 'cz', 'measure']
Coupling map of the backend: [[0, 2], [1, 2], [2, 3], [2, 4]]
Note that for IQM backends the identiy gate id is not actually a gate that is executed on the device and is simply omitted.
At IQM we identify qubits by their names, e.g. ‘QB1’, ‘QB2’, etc. as demonstrated above. In Qiskit, qubits are
identified by their indices in the quantum register, as you can see from the printed coupling map above. Most of the
time you do not need to deal with IQM-style qubit names when using Qiskit, however when you need, the methods
IQMBackend.qubit_name_to_index() and IQMBackend.index_to_qubit_name() can become handy.
Now we can study how the circuit gets transpiled:
from qiskit.compiler import transpile
qc_transpiled = transpile(qc, backend=backend, layout_method='sabre', optimization_level=3)
print(qc_transpiled.draw(output='text'))
global phase: π/2
┌────────────┐┌────────┐ ┌────────────┐┌────────┐ ░ ┌─┐
q_2 -> 0 ┤ R(π/2,π/2) ├┤ R(π,0) ├─────────■───────┤ R(π/2,π/2) ├┤ R(π,0) ├─░───────┤M├
└────────────┘└────────┘ │ └────────────┘└────────┘ ░ └╥┘
ancilla_0 -> 1 ─────────────────────────────────┼─────────────────────────────────────────╫─
┌────────────┐┌────────┐ │ ░ ┌─┐ ║
q_0 -> 2 ┤ R(π/2,π/2) ├┤ R(π,0) ├─■───────■────────────────────────────────░─┤M├────╫─
└────────────┘└────────┘ │ ░ └╥┘ ║
ancilla_1 -> 3 ─────────────────────────┼───────────────────────────────────────────╫─────╫─
┌────────────┐┌────────┐ │ ┌────────────┐ ┌────────┐ ░ ║ ┌─┐ ║
q_1 -> 4 ┤ R(π/2,π/2) ├┤ R(π,0) ├─■─┤ R(π/2,π/2) ├──┤ R(π,0) ├─────────────░──╫─┤M├─╫─
└────────────┘└────────┘ └────────────┘ └────────┘ ░ ║ └╥┘ ║
c_0: ═════════════════════════════════════════════════════════════════════╬══╬══╬═
║ ║ ║
c_1: ═════════════════════════════════════════════════════════════════════╬══╬══╬═
║ ║ ║
c_2: ═════════════════════════════════════════════════════════════════════╬══╬══╬═
║ ║ ║
meas_0: ═════════════════════════════════════════════════════════════════════╩══╬══╬═
║ ║
meas_1: ════════════════════════════════════════════════════════════════════════╩══╬═
║
meas_2: ═══════════════════════════════════════════════════════════════════════════╩═
We also provide an optimization pass specific to the native IQM gate set which aims to reduce the number of single-qubit gates. This optimization expects an already transpiled circuit. As an example, lets apply it to the above circuit:
from iqm.qiskit_iqm.iqm_transpilation import optimize_single_qubit_gates
qc_optimized = optimize_single_qubit_gates(qc_transpiled)
print(qc_optimized.draw(output='text'))
global phase: 3π/2
┌─────────────┐ ┌─────────────┐ ░ ┌─┐
q_0: ┤ R(π/2,3π/2) ├─■─┤ R(π/2,5π/2) ├────────────────░────┤M├───
├─────────────┤ │ └─────────────┘┌─────────────┐ ░ └╥┘┌─┐
q_1: ┤ R(π/2,3π/2) ├─┼────────■───────┤ R(π/2,5π/2) ├─░─────╫─┤M├
├─────────────┤ │ │ └─────────────┘ ░ ┌─┐ ║ └╥┘
q_2: ┤ R(π/2,3π/2) ├─■────────■───────────────────────░─┤M├─╫──╫─
└─────────────┘ ░ └╥┘ ║ ║
q_3: ────────────────────────────────────────────────────╫──╫──╫─
║ ║ ║
q_4: ────────────────────────────────────────────────────╫──╫──╫─
║ ║ ║
c: 3/════════════════════════════════════════════════════╬══╬══╬═
║ ║ ║
meas: 3/════════════════════════════════════════════════════╩══╩══╩═
0 1 2
Under the hood optimize_single_qubit_gates() uses IQMOptimizeSingleQubitGates which inherits from
the Qiskit provided class TransformationPass and can also be used directly if you want to assemble
custom transpilation procedures manually.
Noisy simulation of quantum circuit execution#
The execution of circuits can be simulated locally, with a noise model to mimic the real hardware as much as possible.
To this end, Qiskit on IQM provides the class IQMFakeBackend that can be instantiated with properties of a
certain QPU, or subclasses of it such as IQMFakeAdonis that represent certain quantum architectures with
pre-populated properties and noise model.
from qiskit import execute, QuantumCircuit
from iqm.qiskit_iqm import IQMFakeAdonis
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()
backend = IQMFakeAdonis()
job = execute(circuit, backend, shots=1000)
job.result().get_counts()
Above, we use an IQMFakeAdonis instance to run a noisy simulation of circuit on a simulated 5-qubit Adonis chip.
The noise model includes relaxation (\(T_1\)) and dephasing (\(T_2\)), gate infidelities and readout errors.
If you want to customize the noise model instead of using the default one provided by IQMFakeAdonis, you can create
a copy of the fake Adonis instance with updated error profile:
error_profile = backend.error_profile
error_profile.t1s['QB2'] = 30000.0 # Change T1 time of QB2 as example
custom_fake_backend = backend.copy_with_error_profile(error_profile)
Running a quantum circuit on a facade backend#
Circuits can be executed against a mock environment: an IQM server that has no real quantum computer hardware. Results from such executions are random bits. This may be useful when developing and testing software integrations.
Qiskit on IQM contains IQMFacadeBackend, which allows to combine the mock remote execution with a local
noisy quantum circuit simulation. This way you can both validate your integration as well as get an idea of the expected circuit execution results.
To run a circuit this way, use the facade_adonis backend retrieved from the provider. Note that the provider must be initialized with the URL of a quantum computer with the equivalent architecture (i.e. names of qubits, their connectivity, and the native gateset should match the 5-qubit Adonis architecture).
from qiskit import execute, QuantumCircuit
from iqm.qiskit_iqm import IQMProvider
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()
provider = IQMProvider("https://demo.qc.iqm.fi/cocos/")
backend = provider.get_backend('facade_adonis')
job = execute(circuit, backend, shots=1000)
job.result().get_counts()
Note
When a classical register is added to the circuit, Qiskit fills it with classical bits of value 0 by default. If the
register is not used later, and the circuit is submitted to the IQM server, the results will not contain those
0-filled bits. To make sure the facade backend returns results in the same format as a real IQM server,
IQMFacadeBackend.run() checks for the presence of unused classical registers, and fails with an error if there
are any.
More advanced examples#
In this section we demonstrate some less simple examples of using Qiskit on IQM and its interoperability with various tools available in Qiskit.
It is possible to submit multiple circuits to be executed, as a batch. In many cases this is more time efficient than running the circuits one by one. Batch execution has some restrictions: all the circuits must measure the same qubits, and be executed for the same number of shots. For starters, let’s construct two circuits preparing and measuring different Bell states:
qc_1 = QuantumCircuit(2)
qc_1.h(0)
qc_1.cx(0, 1)
qc_1.measure_all()
qc_2 = QuantumCircuit(2)
qc_2.h(0)
qc_2.x(1)
qc_2.cx(0, 1)
qc_2.measure_all()
Now, we can execute them together in a batch:
job = execute([qc_1, qc_2], backend, initial_layout=[0, 2], shots=1000)
print(job.result().get_counts())
The batch execution functionality can be used to run a parameterized circuit for various concrete values of parameters:
import numpy as np
from qiskit.circuit import Parameter
qc = QuantumCircuit(2)
theta = Parameter('theta')
theta_range = np.linspace(0, 2*np.pi, 3)
qc.h(0)
qc.cx(0, 1)
qc.rz(theta, [0, 1])
qc.cx(0, 1)
qc.h(0)
qc.measure_all()
qc_transpiled = transpile(qc, backend=backend, layout_method='sabre', optimization_level=3)
circuits = [qc_transpiled.bind_parameters({theta: n}) for n in theta_range]
job = execute(circuits, backend, shots=1000, optimization_level=0)
print(job.result().get_counts())
Note that it is important to transpile the parameterized circuit before binding the values to ensure a consistent qubit measurements across circuits in the batch.
How to develop and contribute#
Qiskit on IQM is an open source Python project. You can contribute by creating GitHub issues to report bugs or request new features, or by opening a pull request to submit your own improvements to the codebase.
To start developing the project, clone the GitHub repository and install it in editable mode with all the extras:
$ git clone git@github.com:iqm-finland/qiskit-on-iqm.git
$ cd qiskit-on-iqm
$ pip install -e ".[dev,docs,testing]"
To be able to build the docs graphviz has to be installed. Then to build and view the docs run:
$ tox -e docs
$ firefox build/sphinx/html/index.html
Run the tests:
$ tox
Tagging and releasing#
After implementing changes to Qiskit on IQM one usually wants to release a new version. This means that after the changes are merged to the main branch
the repository should have an updated
CHANGELOG.rstwith information about the new changes,the latest commit should be tagged with the new version number,
and a release should be created based on that tag.
The last two steps are automated, so one needs to worry only about properly updating the CHANGELOG. It should be done along with the pull request which is introducing the main changes. The new version must be added on top of all existing versions and the title must be “Version MAJOR.MINOR”, where MAJOR.MINOR represents the new version number. Please take a look at already existing versions and format the rest of your new CHANGELOG section similarly. Once the pull request is merged into main, a new tag and a release will be created automatically based on the latest version definition in the CHANGELOG.