Devices

In this module, you will learn how to use the qBraid SDK to interface with quantum backends. We will demonstrate how to construct queries and search for available devices using the qbraid.get_devices function, and overview how to execute circuits using the qbraid.providers module.

Unified Device Search

The get_devices function provides a unified quantum device search. It returns a complete list of all quantum backends available through Qiskit, Cirq, and Amazon Braket, along with the “status” of each device, i.e. ONLINE or OFFLINE.

from qbraid import get_devices

get_devices()

There a number of query options available to help filter your search. For example, to find simulators available through AWS or IBM:

get_devices(
    filters={
        "qbraid_id": {"$regex": "sim"},
        "vendor": {"$in": ["AWS", "IBM"]},
    }
)

To search for all gate-based QPUs with at least 7 qubits:

get_devices(
    filters={
        "paradigm": "gate-based",
        "type": "QPU",
        "numberQubits": {"$gte": 7},
    }
)

Or to find all qiskit backends that are online and have less than 50 pending jobs:

get_devices(
    filters={
        "runPackage": "qiskit",
        "pendingJobs": {"$lte": 50},
        "status": "ONLINE",
    }
)

If run in Jupyter, the call above will return a display table similar to the following:

As seen above, qBraid Lab has a built-in Quantum Devices sidebar extension that returns the same data as the get_devices function in an intuitive UI with custom search and filters.

In the lower-right of the table IPython table is the time ellapsed since the last device status update. Device status labels can be manually refreshed by setting refresh=True:

get_devices(refresh=True)

If run in the Python Shell, device data is returned in a similar format.

>>> from qbraid import get_devices
>>> get_devices(filters={"vendor": "AWS", "type": "QPU"}, refresh=True)
Device status updated 0 minutes ago

Device ID                           Status
---------                           ------
aws_ionq_aria1                      ONLINE
aws_oqc_lucy                        ONLINE
aws_quera_aquila                    ONLINE
aws_rigetti_aspen_m2                OFFLINE
aws_rigetti_aspen_m3                ONLINE
aws_xanadu_borealis                 ONLINE

Each supported device is associated with its own qBraid ID. The next section will cover how this value is used to wrap the quantum backends / device objects of various types.

See also

For more on advanced filters options and syntax, see Query Selectors.

Device Wrapper

Given a qbraid_id retrieved from get_devices, a qbraid.providers.QuantumDevice object can be created as follows:

from qbraid import device_wrapper

qbraid_id = 'aws_oqc_lucy'  # as an example

qdevice = device_wrapper(qbraid_id)

From here, class methods are available to get information about the device, execute quantum programs (to be covered in the next section), access the wrapped device object directly, and more.

>>> qdevice.metadata()
{'id': 'arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy',
'name': 'Lucy',
'provider': 'Oxford',
'vendor': 'AWS',
'numQubits': 8,
'deviceType': 'QPU',
'status': 'ONLINE',
'queueDepth': 9,
...,
...}
>>> type(qdevice._device)
braket.aws.aws_device.AwsDevice

Executing Circuits

Each QuantumDevice is equipped with a run method, which extends the wrapped object’s native execute, sample, run, or equivalent circuit execution method. This abstraction allows the user to pass a quantum circuit built using any qbraid-supported frontend to the run method of the wrapped device.

from qiskit import QuantumCircuit

def circuit0():
    circuit = QuantumCircuit(2)
    circuit.h(0)
    circuit.cx(0,1)
    return circuit

from cirq import Circuit, LineQubit, ops

def circuit1():
    q0, q1 = LineQubit.range(2)
    circuit = Circuit(ops.H(q0), ops.CNOT(q0, q1))
    return circuit

>>> qiskit_circuit = circuit0()
>>> cirq_circuit = circuit1()
>>> qjob0 = qdevice.run(qiskit_circuit)
>>> qjob1 = qdevice.run(cirq_circuit)

Above, I defined two quantum programs, one using qiskit and the other using cirq, and executed each on Oxford Quantum Circuit’s Lucy QPU, made available through Amazon Braket.

Example Flow: Least Busy QPU

In this section, we’ll piece together a workflow example, starting by using the ibm_least_busy_qpu function to get the qbraid_id of the IBMQ QPU with the least number of queued quantum jobs.

>>> from qbraid.providers.ibm import ibm_least_busy_qpu
>>> qbraid_id = ibm_least_busy_qpu()
>>> qdevice = device_wrapper(qbraid_id)
>>> qdevice.name
'Nairobi'
>>> qdevice.status()
<DeviceStatus.ONLINE: 0>

After applying the device wrapper and verifying the device is online, we’re ready to submit a job. This time, we’ll use a Cirq circuit as the run method input.

>>> from qbraid.interface import random_circuit
>>> cirq_circuit = random_circuit("cirq", num_qubits=qdevice.num_qubits)
>>> qdevice.queue_depth()
4
>>> qjob = qdevice.run(cirq_circuit)
>>> qjob.status()
<JobStatus.QUEUED: 1>
>>> qdevice.queue_depth()
5

For fun, we the set number of qubits used in the random circuit equal to the number of qubits supported by the backend. We then checked the backend’s number of pending jobs, and saw the number increase by one after submitting our job.

Summary

The device layer of the qBraid SDK enables users to execute quantum circuits of any qbraid.QPROGRAM_TYPES on any simulator or QPU returned by qbraid.get_devices. Filter your search to the specifications of your task, identify a device, and execute your program through a consistent three-step protocol:

1. Get qbraid device ID

2. Apply device wrapper

3. Execute program via run method