pytket-qiskit#

IBM’s Qiskit is an open-source framework for quantum computation, ranging from high-level algorithms to low-level circuit representations, simulation and access to the IBMQ Experience devices.

pytket-qiskit is an extension to pytket that allows pytket circuits to be run on IBM backends and simulators, as well as conversion to and from Qiskit representations.

pytket-qiskit is available for Python 3.10, 3.11 and 3.12, on Linux, MacOS and Windows. To install, run:

pip install pytket-qiskit

This will install pytket if it isn’t already installed, and add new classes and methods into the pytket.extensions namespace.

Available IBM Backends#

IBMQBackend

A backend for running circuits on remote IBMQ devices.

IBMQEmulatorBackend

A Backend which uses the ibmq_qasm_simulator to emulate the behaviour of IBMQBackend.

IBMQLocalEmulatorBackend

A backend which uses the AerBackend to loaclly emulate the behaviour of IBMQBackend.

AerBackend

Backend for running simulations on the Qiskit Aer QASM simulator.

AerStateBackend

Backend for running simulations on the Qiskit Aer Statevector simulator.

AerUnitaryBackend

Backend for running simulations on the Qiskit Aer Unitary simulator.

An example using the shots-based AerBackend simulator is shown below.

from pytket.extensions.qiskit import AerBackend
from pytket import Circuit

backend = AerBackend()
circ = Circuit(2).H(0).CX(0, 1).measure_all()

# Compilation not needed here as both H and CX are supported gates
result = backend.run_circuit(circ, n_shots=1000)

This simulator supports a large set of gates and by default has no architectural constraints or quantum noise. However the user can pass in a noise model or custom architecture to more closely model a real quantum device.

The AerBackend also supports GPU simulation which can be configured as follows.

from pytket.extensions.qiskit import AerBackend

backend = AerBackend()
backend._qiskit_backend.set_option("device", "GPU")

Note

Making use of GPU simulation requires the qiskit-aer-gpu package. This can be installed with the command

pip install qiskit-aer-gpu

Access and Credentials#

With the exception of the Aer simulators, accessing devices and simulators through the pytket-qiskit extension requires an IBM account. An account can be set up here: https://quantum-computing.ibm.com/login.

Once you have created an account you can obtain an API token which you can use to configure your credentials locally.

In this section we are assuming that you have set the following variables with the corresponding values:

# Replace the placeholders with your actual values
ibm_token = '<your_ibm_token_here>'
hub = '<your_hub_here>'
group = '<your_group_here>'
project = '<your_project_here>'

Method 1: Using IBMProvider#

You can use the following qiskit commands to save your IBM credentials to disk:

from qiskit_ibm_provider import IBMProvider
from qiskit_ibm_runtime import QiskitRuntimeService

IBMProvider.save_account(token=ibm_token)
QiskitRuntimeService.save_account(channel="ibm_quantum", token=ibm_token)

To see which devices you can access you can use the available_devices method on the IBMQBackend or IBMQEmulatorBackend. Note that it is possible to pass optional instance and provider arguments to this method. This allows you to see which devices are accessible through your IBM hub.

from pytket.extensions.qiskit import IBMQBackend
from qiskit_ibm_provider import IBMProvider

my_instance=f"{hub}/{group}/{project}"
ibm_provider = IBMProvider(instance=my_instance)
backend = IBMQBackend("ibmq_nairobi") # Initialise backend for an IBM device

backendinfo_list = backend.available_devices(instance=my_instance, provider=ibm_provider)
print([backend.device_name for backend in backendinfo_list])

Method 2: Saving credentials in a local pytket config file#

Alternatively, you can store your credentials in local pytket config using the set_ibmq_config() method.

from pytket.extensions.qiskit import set_ibmq_config

set_ibmq_config(ibmq_api_token=ibm_token)

After saving your credentials you can access pytket-qiskit backend repeatedly without having to re-initialise your credentials.

If you are a member of an IBM hub then you can add this information to set_ibmq_config as well.

from pytket.extensions.qiskit import set_ibmq_config

set_ibmq_config(ibmq_api_token=ibm_token, instance=f"{hub}/{group}/{project}")

QiskitConfig

Holds config parameters for pytket-qiskit.

set_ibmq_config

Set default values for any of hub, group, project or API token for your IBMQ provider.

Converting circuits between pytket and qiskit#

Users may wish to port quantum circuits between pytket and qiskit. This allows the features of both libraries to be used. For instance those familiar with qiskit may wish to convert their circuits to pytket and use the available compilation passes to optimise circuits.

qiskit_to_tk

Converts a qiskit qiskit.QuantumCircuit to a pytket Circuit.

tk_to_qiskit

Converts a pytket Circuit to a qiskit qiskit.QuantumCircuit.

Default Compilation#

Every Backend in pytket has its own default_compilation_pass method. This method applies a sequence of optimisations to a circuit depending on the value of an optimisation_level parameter. This default compilation will ensure that the circuit meets all the constraints required to run on the Backend. The passes applied by different levels of optimisation are specified in the table below.

Default compilation pass for the IBMQBackend, IBMQEmulatorBackend and IBMQLocalEmulatorBackend#

optimisation_level = 0

optimisation_level = 1

optimisation_level = 2 [1]

DecomposeBoxes

DecomposeBoxes

DecomposeBoxes

self.rebase_pass [2]

SynthesiseTket

FullPeepholeOptimise

CXMappingPass [3]

CXMappingPass [3]

CXMappingPass [3]

NaivePlacementPass

NaivePlacementPass

NaivePlacementPass

self.rebase_pass [2]

SynthesiseTket

KAKDecomposition(allow_swaps=False)

RemoveRedundancies

self.rebase_pass [2]

CliffordSimp(allow_swaps=False)

RemoveRedundancies

SynthesiseTket

self.rebase_pass [2]

RemoveRedundancies

  • [1] If no value is specified then optimisation_level defaults to a value of 2.

  • [2] self.rebase_pass is a rebase to the gateset supported by the backend. For IBM quantum devices and emulators that is either {X, SX, Rz, CX} or {X, SX, Rz, ECR}. The more idealised Aer simulators have a much broader range of supported gates.

  • [3] Here CXMappingPass maps program qubits to the architecture using a NoiseAwarePlacement

Note: The default_compilation_pass for AerBackend is the same as above.

Noise Modelling#

CrosstalkParams

Stores various parameters for modelling crosstalk noise

Using TKET directly on qiskit circuits#

For usage of TketBackend see the qiskit integration notebook example.

TketBackend

Wraps a Backend as a qiskit.providers.BaseBackend for use within the Qiskit software stack.

TketPass

The tket compiler to be plugged in to the Qiskit compilation sequence

TketAutoPass

The tket compiler to be plugged in to the Qiskit compilation sequence

TketJob

TketJob wraps a ResultHandle list as a qiskit.providers.JobV1