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 IBM quantum devices and simulators.
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¶
A backend for running circuits on remote IBMQ devices. |
|
A backend which uses the AerBackend to loaclly emulate the behaviour of IBMQBackend. |
|
Backend for running simulations on the Qiskit Aer QASM simulator. |
|
Backend for running simulations on the Qiskit Aer Statevector simulator. |
|
Backend for running simulations on the Qiskit Aer Unitary simulator. |
|
Backend for running simulations on the Qiskit Aer density matrix 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.ibm.com/.
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>'
inst = f"{hub}/{group}/{project}"
Method 1: Using QiskitRuntimeService
¶
You can use the following qiskit commands to save your IBM credentials to disk:
from qiskit_ibm_runtime import QiskitRuntimeService
QiskitRuntimeService.save_account(channel="ibm_quantum", token=ibm_token, instance=inst)
To see which devices you can access, use the IBMQBackend.available_devices()
method. Note that it is possible to pass an optional instance
argument to this method. This allows you to see which IBM devices are accessible with your credentials.
from pytket.extensions.qiskit import IBMQBackend
backend = IBMQBackend("ibm_kyiv") # Initialise backend for an IBM device
backendinfo_list = backend.available_devices(instance=inst)
print([backend.device_name for backend in backendinfo_list])
For more information, see the documentation for qiskit-ibm-runtime.
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}")
Holds config parameters for pytket-qiskit. |
|
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.
Converts a qiskit |
|
Converts a pytket |
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. Note that optimisation levels 0, 1 and
2 preserve barriers in a circuit, while optimisation level 3 will remove them.
optimisation_level = 0 |
optimisation_level = 1 |
optimisation_level = 2 [1] |
optimisation_level = 3 |
---|---|---|---|
LightSabre [3] |
LightSabre [3] |
LightSabre [3] |
|
LightSabre [3] |
|||
[1] If no value is specified then
optimisation_level
defaults to a value of 2.[2]
AutoRebase
is a conversion to the gateset supported by the backend. For IBM quantum devices and emulators the supported gate set is either , , or . The more idealised Aer simulators have a much broader range of supported gates.[3] This is imported from qiskit and corresponds to the method in “LightSABRE: A Lightweight and Enhanced SABRE Algorithm”, Henry Zou, Matthew Treinish, Kevin Hartman, Alexander Ivrii, Jake Lishman, arXiv:2409.08368.
Note: The default_compilation_pass()
for AerBackend
is the same as above if a NoiseModel
is used. A NoiseModel
implicitly defines connectivity constraints via edge errors. If no NoiseModel
is used then then any passes related to connectivity constraints are omitted from the default_compilation_pass()
for AerBackend
.
Noise Modelling¶
Stores various parameters for modelling crosstalk noise |
Using TKET directly on qiskit circuits¶
For usage of TketBackend
see the qiskit integration notebook example.
Wraps a |
|
The tket compiler to be plugged in to the Qiskit compilation sequence |
|
The tket compiler to be plugged in to the Qiskit compilation sequence |
|
TketJob wraps a |
- API documentation
IBMQBackend
IBMQBackend.__init__()
IBMQBackend.available_devices()
IBMQBackend.cancel()
IBMQBackend.circuit_status()
IBMQBackend.default_compilation_pass()
IBMQBackend.get_compiled_circuit()
IBMQBackend.get_compiled_circuits()
IBMQBackend.get_result()
IBMQBackend.process_circuits()
IBMQBackend.rebase_pass()
IBMQBackend.backend_info
IBMQBackend.required_predicates
IBMQEmulatorBackend
IBMQEmulatorBackend.__init__()
IBMQEmulatorBackend.cancel()
IBMQEmulatorBackend.circuit_status()
IBMQEmulatorBackend.default_compilation_pass()
IBMQEmulatorBackend.get_result()
IBMQEmulatorBackend.process_circuits()
IBMQEmulatorBackend.rebase_pass()
IBMQEmulatorBackend.backend_info
IBMQEmulatorBackend.required_predicates
AerBackend
AerBackend.__init__()
AerBackend.available_devices()
AerBackend.cancel()
AerBackend.circuit_status()
AerBackend.default_compilation_pass()
AerBackend.empty_cache()
AerBackend.get_compiled_circuit()
AerBackend.get_compiled_circuits()
AerBackend.get_operator_expectation_value()
AerBackend.get_pauli_expectation_value()
AerBackend.get_result()
AerBackend.get_results()
AerBackend.pop_result()
AerBackend.process_circuit()
AerBackend.process_circuits()
AerBackend.rebase_pass()
AerBackend.run_circuit()
AerBackend.run_circuits()
AerBackend.valid_circuit()
AerBackend.backend_info
AerBackend.expectation_allows_nonhermitian
AerBackend.persistent_handles
AerBackend.required_predicates
AerBackend.supports_contextual_optimisation
AerBackend.supports_counts
AerBackend.supports_density_matrix
AerBackend.supports_expectation
AerBackend.supports_shots
AerBackend.supports_state
AerBackend.supports_unitary
AerStateBackend
AerStateBackend.__init__()
AerStateBackend.available_devices()
AerStateBackend.cancel()
AerStateBackend.circuit_status()
AerStateBackend.default_compilation_pass()
AerStateBackend.empty_cache()
AerStateBackend.get_compiled_circuit()
AerStateBackend.get_compiled_circuits()
AerStateBackend.get_operator_expectation_value()
AerStateBackend.get_pauli_expectation_value()
AerStateBackend.get_result()
AerStateBackend.get_results()
AerStateBackend.pop_result()
AerStateBackend.process_circuit()
AerStateBackend.process_circuits()
AerStateBackend.rebase_pass()
AerStateBackend.run_circuit()
AerStateBackend.run_circuits()
AerStateBackend.valid_circuit()
AerStateBackend.backend_info
AerStateBackend.expectation_allows_nonhermitian
AerStateBackend.persistent_handles
AerStateBackend.required_predicates
AerStateBackend.supports_contextual_optimisation
AerStateBackend.supports_counts
AerStateBackend.supports_density_matrix
AerStateBackend.supports_expectation
AerStateBackend.supports_shots
AerStateBackend.supports_state
AerStateBackend.supports_unitary
AerUnitaryBackend
AerUnitaryBackend.__init__()
AerUnitaryBackend.available_devices()
AerUnitaryBackend.cancel()
AerUnitaryBackend.circuit_status()
AerUnitaryBackend.default_compilation_pass()
AerUnitaryBackend.empty_cache()
AerUnitaryBackend.get_compiled_circuit()
AerUnitaryBackend.get_compiled_circuits()
AerUnitaryBackend.get_operator_expectation_value()
AerUnitaryBackend.get_pauli_expectation_value()
AerUnitaryBackend.get_result()
AerUnitaryBackend.get_results()
AerUnitaryBackend.pop_result()
AerUnitaryBackend.process_circuit()
AerUnitaryBackend.process_circuits()
AerUnitaryBackend.rebase_pass()
AerUnitaryBackend.run_circuit()
AerUnitaryBackend.run_circuits()
AerUnitaryBackend.valid_circuit()
AerUnitaryBackend.backend_info
AerUnitaryBackend.expectation_allows_nonhermitian
AerUnitaryBackend.persistent_handles
AerUnitaryBackend.required_predicates
AerUnitaryBackend.supports_contextual_optimisation
AerUnitaryBackend.supports_counts
AerUnitaryBackend.supports_density_matrix
AerUnitaryBackend.supports_expectation
AerUnitaryBackend.supports_shots
AerUnitaryBackend.supports_state
AerUnitaryBackend.supports_unitary
AerDensityMatrixBackend
AerDensityMatrixBackend.__init__()
AerDensityMatrixBackend.available_devices()
AerDensityMatrixBackend.cancel()
AerDensityMatrixBackend.circuit_status()
AerDensityMatrixBackend.default_compilation_pass()
AerDensityMatrixBackend.empty_cache()
AerDensityMatrixBackend.get_compiled_circuit()
AerDensityMatrixBackend.get_compiled_circuits()
AerDensityMatrixBackend.get_operator_expectation_value()
AerDensityMatrixBackend.get_pauli_expectation_value()
AerDensityMatrixBackend.get_result()
AerDensityMatrixBackend.get_results()
AerDensityMatrixBackend.pop_result()
AerDensityMatrixBackend.process_circuit()
AerDensityMatrixBackend.process_circuits()
AerDensityMatrixBackend.rebase_pass()
AerDensityMatrixBackend.run_circuit()
AerDensityMatrixBackend.run_circuits()
AerDensityMatrixBackend.valid_circuit()
AerDensityMatrixBackend.backend_info
AerDensityMatrixBackend.expectation_allows_nonhermitian
AerDensityMatrixBackend.persistent_handles
AerDensityMatrixBackend.required_predicates
AerDensityMatrixBackend.supports_contextual_optimisation
AerDensityMatrixBackend.supports_counts
AerDensityMatrixBackend.supports_density_matrix
AerDensityMatrixBackend.supports_expectation
AerDensityMatrixBackend.supports_shots
AerDensityMatrixBackend.supports_state
AerDensityMatrixBackend.supports_unitary
process_characterisation()
qiskit_to_tk()
tk_to_qiskit()
TketBackend
CrosstalkParams
TketAutoPass
TketPass
JobInfo
TketJob
QiskitConfig
set_ibmq_config()
- Changelog
- 0.61.0 (December 2024)
- 0.60.0 (November 2024)
- 0.59.0 (November 2024)
- 0.58.0 (October 2024)
- 0.57.0 (October 2024)
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- 0.55.0 (July 2024)
- 0.54.1 (June 2024)
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- 0.50.0 (March 2024)
- 0.49.0 (March 2024)
- 0.48.1rc1
- 0.48.0 (January 2024)
- 0.47.0 (January 2024)
- 0.46.0 (November 2023)
- 0.45.0 (October 2023)
- 0.44.0 (September 2023)
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- 0.40.0 (June 2023)
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- 0.37.1 (March 2023)
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- 0.35.0 (February 2023)
- 0.34.0 (January 2023)
- 0.33.0 (December 2022)
- 0.32.0 (December 2022)
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- 0.20.0 (November 2021)
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- 0.16.1 (July 2021)
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- 0.15.1 (July 2021)
- 0.15.0 (June 2021)
- 0.14.0 (unreleased)
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- 0.10.0 (April 2021)