Providers Interface

qiskit.providers

This module contains the classes used to build external providers for Terra. A provider is anything that provides an external service to Terra. The typical example of this is a Backend provider which provides Backend objects which can be used for executing QuantumCircuit and/or Schedule objects. This module contains the abstract classes which are used to define the interface between a provider and terra.

Version Support

Each providers interface abstract class is individually versioned. When we need to make a change to an interface a new abstract class will be created to define the new interface. These interface changes are not guaranteed to be backwards compatible between versions.

Version Changes

Each minor version release of qiskit-terra may increment the version of any providers interface a single version number. It will be an aggregate of all the interface changes for that release on that interface.

Version Support Policy

To enable providers to have time to adjust to changes in this interface Terra will support support multiple versions of each class at once. Given the nature of one version per release the version deprecation policy is a bit more conservative than the standard deprecation policy. Terra will support a provider interface version for a minimum of 3 minor releases or the first release after 6 months from the release that introduced a version, whichever is longer, prior to a potential deprecation. After that the standard deprecation policy will apply to that interface version. This will give providers and users sufficient time to adapt to potential breaking changes in the interface. So for example lets say in 0.19.0 BackendV2 is introduced and in the 3 months after the release of 0.19.0 we release 0.20.0, 0.21.0, and 0.22.0, then 7 months after 0.19.0 we release 0.23.0. In 0.23.0 we can deprecate BackendV2, and it needs to still be supported and can’t be removed until the deprecation policy completes.

It’s worth pointing out that Terra’s version support policy doesn’t mean providers themselves will have the same support story, they can (and arguably should) update to newer versions as soon as they can, the support window is just for Terra’s supported versions. Part of this lengthy window prior to deprecation is to give providers enough time to do their own deprecation of a potential end user impacting change in a user facing part of the interface prior to bumping their version. For example, lets say we changed the signature to Backend.run() in BackendV34 in a backwards incompatible way, before Aer could update its AerBackend class to use version 34 they’d need to deprecate the old signature prior to switching over. The changeover for Aer is not guaranteed to be lockstep with Terra so we need to ensure there is a sufficient amount of time for Aer to complete it’s deprecation cycle prior to removing version 33 (ie making version 34 mandatory/the minimum version).

Abstract Classes

Provider

Backend

Options

Job

Writing a New Provider

If you have a quantum device or simulator that you would like to integrate with Qiskit you will need to write a provider. A provider will provide Terra with a method to get available BackendV1 objects. The BackendV1 object provides both information describing a backend and its operation for the transpiler so that circuits can be compiled to something that is optimized and can execute on the backend. It also provides the run() method which can run the QuantumCircuit objects and/or Schedule objects. This enables users and other Qiskit APIs, such as execute() and higher level algorithms in Qiskit Aqua, to get results from executing circuits on devices in a standard fashion regardless of how the Backend is implemented. At a high level the basic steps for writing a provider are:

• Implement a ProviderV1 subclass that handles access to the backend(s).

• Implement a BackendV1 subclass and its run() method.

  • Add any custom gates for the backend’s basis to the session EquivalenceLibrary instance.

• Implement a JobV1 subclass that handles interacting with a running job.

For a simple example of a provider the qiskit-aqt-provider package is worth using as an example.

Provider

A provider class serves a single purpose: to get backend objects that enable executing circuits on a device or simulator. The expectation is that any required credentials and/or authentication will be handled in the initialization of a provider object. The provider object will then provide a list of backends, and methods to filter and acquire backends (using the provided credentials if required). An example provider class looks like:

from qiskit.providers import ProviderV1 as Provider
from qiskit.providers.providerutils import filter_backends
 
from .backend import MyBackend
 
class MyProvider(Provider):
 
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.backends = [MyBackend(provider=self)]
 
    def backends(self, name=None, **kwargs):
        if name:
            backends = [
                backend for backend in backends if backend.name() == name]
        return filter_backends(backends, filters=filters, **kwargs)

Ensure that any necessary information for authentication (if required) are present in the class and that the backends method matches the required interface. The rest is up to the specific provider on how to implement.

Backend

The backend classes are the core to the provider. These classes are what provide the interface between Qiskit and the hardware or simulator that will execute circuits. This includes providing the necessary information to describe a backend to the compiler so that it can embed and optimize any circuit for the backend. There are 3 required things in every backend object: a backend configuration property, a run() method, and a _default_options() method. For example, a minimum working example would be something like:

from qiskit.providers import BackendV1 as Backend
from qiskit.providers.models import BackendConfiguration
from qiskit.providers import Options
 
 
class Mybackend(Backend):
 
    configuration = {
        'backend_name': 'my_backend',
        'backend_version': '1.0.0',
        'n_qubits': 5,
        'basis_gates': ['p', 'sx', 'u', 'cx', 'id'],
        'coupling_map': [[0, 1], [1, 2], [2, 3], [3, 4]],
        'simulator': False,
        'local': False,
        'conditional': True,
        'open_pulse': False,
        'memory': True,
        'max_shots': 8196,
        'gates': []
    }
 
    def __init(self, provider):
        super().__init__(
            configuration=BackendConfiguration.from_dict(self.configuration),
            provider=provider)
 
    @classmethod
    def _default_options(cls):
        return Options(shots=1024, memory=False)
 
    def run(circuits, **kwargs):
        # serialize circuits submit to backend and create a job
        for kwarg in kwargs:
            if not hasattr(kwarg, self.options):
                warnings.warn(
                    "Option %s is not used by this backend" % kwarg,
                    UserWarning, stacklevel=2)
        options = {
            'shots': kwargs.get('shots', self.options.shots),
            'memory': kwargs.get('memory', self.options.shots),
        }
        job_json = convert_to_wire_format(circuit, options)
        job_handle = submit_to_backend(job_jsonb)
        return MyJob(self. job_handle, job_json, circuit)

Transpiler Interface

The key piece of the backend object is how it describes itself to the compiler. This is composed of three classes, the BackendConfiguration which describes the immutable properties of the backend (things like the number of qubits, coupling map, etc), the BackendProperties which describes measured properties of the device (such as current error rates), and the PulseDefaults which defines the default pulse behavior for the backend (such as the pulse sequences for gates). Of these only BackendConfiguration is required.

It’s worth noting that this interface will likely evolve over time as the compiler grows and improves, thus requiring new information from the backends. This is a key reason why the providers interfaces are versioned to enable making these changes in a controlled manner. The details here will change and reflect the latest version of this interface.

Custom Basis Gates

1. If your backend doesn’t use gates in the Qiskit circuit library (qiskit.circuit.library) you can integrate support for this into your provider. The basic method for doing this is first to define a Gate class for all the custom gates in the basis set. For example:

import numpy as np
 
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
 
class SYGate(Gate):
    def __init__(self, label=None):
        super().__init__("sy", 1, [], label=label)
 
    def _define(self):
        qc = QuantumCircuit(1)
        q.ry(np.pi / 2, 0)
        self.definition = qc

The key thing to ensure is that for any custom gates in your Backend’s basis set your custom gate’s name attribute (the first param on super().__init__() in the __init__ definition above) does not conflict with the name of any other gates. The name attribute is what is used to identify the gate in the basis set for the transpiler. If there is a conflict the transpiler will not know which gate to use.

2. After you’ve defined the custom gates to use for the Backend’s basis set then you need to add equivalence rules to the standard equivalence library so that the transpile() function and transpiler module can convert an arbitrary circuit using the custom basis set. This can be done by defining equivalent circuits, in terms of the custom gate, for standard gates. Typically if you can convert from a CXGate (if your basis doesn’t include a standard 2 qubit gate) and some commonly used single qubit rotation gates like the HGate and UGate that should be sufficient for the transpiler to translate any circuit into the custom basis gates. But, the more equivalence rules that are defined from standard gates to your basis the more efficient translation from an arbitrary circuit to the target basis will be (although not always, and there is a diminishing margin of return).

For example, if you were to add some rules for the above custom SYGate we could define the U2Gate and HGate:

from qiskit.circuit.equivalence_library import SessionEquivalenceLibrary
from qiskit.circuit.library import HGate
from qiskit.circuit.library import ZGate
from qiskit.circuit.library import RZGate
from qiskit.circuit.library import U2Gate
 
 
# H => Z SY
q = qiskit.QuantumRegister(1, "q")
def_sy_h = qiskit.QuantumCircuit(q)
def_sy_h.append(ZGate(), [q[0]], [])
def_sy_h.append(SYGate(), [q[0]], [])
SessionEquivalenceLibrary.add_equivalence(
    HGate(), def_sy_h)
 
# u2 => Z SY Z
phi = qiskit.circuit.Parameter('phi')
lam = qiskit.circuit.Parameter('lambda')
q = qiskit.QuantumRegister(1, "q")
def_sy_u2 = qiskit.QuantumCircuit(q)
def_sy_u2.append(RZGate(lam), [q[0]], [])
def_sy_u2.append(SYGate(), [q[0]], [])
def_sy_u2.append(RZGate(phi), [q[0]], [])
SessionEquivalenceLibrary.add_equivalence(
    U2Gate(phi, lam), def_sy_u2)

You will want this to be run on import so that as soon as the provider’s package is imported it will be run. This will ensure that any time the BasisTranslator pass is run with the custom gates the equivalence rules are defined.

It’s also worth noting that depending on the basis you’re using, some optimization passes in the transpiler, such as Optimize1qGatesDecomposition, may not be able to operate with your custom basis. For our SYGate example, the Optimize1qGatesDecomposition will not be able to simplify runs of single qubit gates into the SY basis. This is because the OneQubitEulerDecomposer class does not know how to work in the SY basis. To solve this the SYGate class would need to be added to Qiskit and OneQubitEulerDecomposer updated to support decomposing to the SYGate. Longer term that is likely a better direction for custom basis gates and contributing the definitions and support in the transpiler will ensure that it continues to be well supported by Qiskit moving forward.

Run Method

Of key importance is the run() method, which is used to actually submit circuits to a device or simulator. The run method handles submitting the circuits to the backend to be executed and returning a Job object. Depending on the type of backend this typically involves serializing the circuit object into the API format used by a backend. For example, on IBMQ backends from the qiskit-ibmq-provider package this involves converting from a quantum circuit and options into a qobj JSON payload and submitting that to the IBM Quantum API. Since every backend interface is different (and in the case of the local simulators serialization may not be needed) it is expected that the backend’s run() method will handle this conversion.

An example run method would be something like:

def run(self, circuits. **kwargs):
    for kwarg in kwargs:
        if not hasattr(kwarg, self.options):
            warnings.warn(
                "Option %s is not used by this backend" % kwarg,
                UserWarning, stacklevel=2)
    options = {
        'shots': kwargs.get('shots', self.options.shots)
        'memory': kwargs.get('memory', self.options.shots),
    }
    job_json = convert_to_wire_format(circuit, options)
    job_handle = submit_to_backend(job_jsonb)
    return MyJob(self. job_handle, job_json, circuit)

Options

There are often several options for a backend that control how a circuit is run. The typical example of this is something like the number of shots which is how many times the circuit is to be executed. The options available for a backend are defined using an Options object. This object is initially created by the _default_options method of a Backend class. The default options returns an initialized Options object with all the default values for all the options a backend supports. For example, if the backend supports only supports shots the _default_options method would look like:

@classmethod
def _default_options(cls):
    return Options(shots=1024)

Job

The output from the run() method is a JobV1 object. Each provider is expected to implement a custom job subclass that defines the behavior for the provider. There are 2 types of jobs depending on the backend’s execution method, either a sync or async. By default jobs are considered async and the expectation is that it represents a handle to the async execution of the circuits submitted with Backend.run(). An async Job object provides users the ability to query the status of the execution, cancel a running job, and block until the execution is finished. The result is the primary user facing method which will block until the execution is complete and then will return a Result object with results of the job.

For some backends (mainly local simulators) the execution of circuits is a synchronous operation and there is no need to return a handle to a running job elsewhere. For sync jobs its expected that the run() method on the backend will block until a Result object is generated and the sync job will return with that inner Result object.

An example job class for an async API based backend would look something like:

from qiskit.providers import JobV1 as Job
from qiskit.providers import JobError
from qiskit.providers import JobTimeoutError
from qiskit.providers.jobstatus import JobStatus
from qiskit.result import Result
 
 
class MyJob(Job):
    def __init__(self, backend, job_id, job_json, circuits):
        super().__init__(backend, job_id)
        self._backend = backend
        self.job_json = job_json
        self.circuits = circuits
 
    def _wait_for_result(self, timeout=None, wait=5):
        start_time = time.time()
        result = None
        while True:
            elapsed = time.time() - start_time
            if timeout and elapsed >= timeout:
                raise JobTimeoutError('Timed out waiting for result')
            result = get_job_status(self._job_id)
            if result['status'] == 'complete':
                break
            if result['status'] == 'error':
                raise JobError('Job error')
            time.sleep(wait)
        return result
 
    def result(self, timeout=None, wait=5):
        result = self._wait_for_result(timeout, wait)
        results = [{'success': True, 'shots': len(result['counts']),
                    'data': result['counts']}]
        return Result.from_dict({
            'results': results,
            'backend_name': self._backend.configuration().backend_name,
            'backend_version': self._backend.configuration().backend_version,
            'job_id': self._job_id,
            'qobj_id': ', '.join(x.name for x in self.circuits),
            'success': True,
        })
 
    def status(self):
        result = get_job_status(self._job_id)
        if result['status'] == 'running':
            status = JobStatus.RUNNING
        elif result['status'] == 'complete':
            status = JobStatus.DONE
        else:
            status = JobStatus.ERROR
        return status
 
def submit(self):
    raise NotImplementedError

and for a sync job:

class MySyncJob(Job):
    _async = False
 
    def __init__(self, backend, job_id, result):
        super().__init__(backend, job_id)
        self._result = result
 
    def submit(self):
        return
 
    def result(self):
        return self._result
 
    def status(self):
        return JobStatus.DONE

Legacy Provider Interface Base Objects

qiskit.providers

Base Objects

Job Status

Exceptions