The qBraid-SDK is a platform-agnostic quantum runtime framework designed for both quantum software and hardware providers.

This Python-based tool streamlines the full lifecycle management of quantum jobs—from defining program specifications to job submission and through to the post-processing and visualization of results. Unlike existing runtime frameworks that focus their automation and abstractions on quantum components, qBraid adds an extra layer of abstractions that considers the ultimate IR needed to encode the quantum program and securely submit it to a remote API. Notably, the qBraid-SDK does not adhere to a fixed circuit-building library, or quantum program representation. Instead, it empowers providers to dynamically register any desired input program type as the target based on their specific needs. This flexibility is extended by the framework’s modular pipeline, which facilitates any number of additional program validation, transpilation, and compilation steps.

By addressing the full scope of client-side software requirements necessary for secure submission and management of quantum jobs, the qBraid-SDK vastly reduces the overhead and redundancy typically associated with the development of internal pipelines and cross-platform integrations in quantum computing.

Resources

• User Guide
• API Reference
• Example Notebooks

Installation & Setup

For the best experience, install the qBraid-SDK environment on lab.qbraid.com. Login (or create an account) and follow the steps to install an environment. Using the SDK on qBraid Lab means direct, pre-configured access to QPUs from IonQ, Oxford Quantum Circuits, QuEra, Rigetti, and IQM, as well as on-demand simulators from qBraid, AWS, IonQ, QuEra, and NEC. See qBraid Quantum Jobs and pricing for more.

Local install

The qBraid-SDK, and all of its dependencies, can be installed using pip:

bash pip install qbraid

You can also install from source by cloning this repository and running a pip install command in the root directory of the repository:

bash git clone https://github.com/qBraid/qBraid.git cd qBraid pip install .

Note: The qBraid-SDK requires Python 3.10 or greater.

To use qBraid Runtime locally, you must also install the necessary extras and configure your account credentials according to the device(s) that you are targeting. Follow the linked, provider-specific, instructions for the QbraidProvider, BraketProvider, QiskitRuntimeProvider, IonQProvider, OQCProvider, and AzureQuantumProvider, as applicable.

Quickstart

Check version

You can view the version of the qBraid-SDK you have installed and get detailed information about the installation within Python using the following commands:

```python In [1]: import qbraid

In [2]: qbraid.version

In [3]: qbraid.about() ```

Transpiler

Graph-based approach to quantum program type conversions.

Below, QPROGRAM_REGISTRY maps shorthand identifiers for supported quantum programs, each corresponding to a type in the typed QPROGRAM Union. For example, 'qiskit' maps to qiskit.QuantumCircuit in QPROGRAM. Notably, 'qasm2' and 'qasm3' both represent raw OpenQASM strings. This arrangement simplifies targeting and transpiling between different quantum programming frameworks.

```python

from qbraid import QPROGRAMREGISTRY QPROGRAMREGISTRY {'cirq': cirq.circuits.circuit.Circuit, 'qiskit': qiskit.circuit.quantumcircuit.QuantumCircuit, 'pennylane': pennylane.tape.tape.QuantumTape, 'pyquil': pyquil.quil.Program, 'pytket': pytket.tket.circuit.Circuit, 'braket': braket.circuits.circuit.Circuit, 'braketahs': braket.ahs.analoghamiltoniansimulation.AnalogHamiltonianSimulation, 'openqasm3': openqasm3.ast.Program, 'pyqir': pyqir.Module, 'cpppyqubo': cpppyqubo.Model, 'qasm2': str, 'qasm3': str, 'qasm2kirin': str, 'ionq': qbraid.programs.typer.IonQDict, 'qubo': qbraid.programs.typer.QuboCoefficientsDict, 'bloqade': bloqade.analog.builder.assign.BatchAssign, 'cudaq': cudaq.kernel.kernelbuilder.PyKernel, 'qibo': qibo.models.circuit.Circuit, 'stim': stim.stimsse2.Circuit, 'pulser': pulser.sequence.sequence.Sequence, 'pyqpanda3': pyqpanda3.core.QProg, 'autoqasm': autoqasm.program.program.Program} ```

Pass any registered quantum program along with a target package from QPROGRAM_REGISTRY to "transpile" your circuit to a new program type:

```python

from qbraid import randomcircuit, transpile qiskitcircuit = randomcircuit("qiskit") cirqcircuit = transpile(qiskitcircuit, "cirq") print(qiskitcircuit) ┌────────────┐ q0: ──■──┤ Rx(3.0353) ├ ┌─┴─┐└───┬────┬───┘ q1: ┤ H ├────┤ √X ├──── └───┘ └────┘ print(cirq_circuit) 0: ───@───Rx(0.966π)─── │ 1: ───H───X^0.5──────── ```

Behind the scenes, the qBraid-SDK uses rustworkx to create a directional graph that maps all possible conversions between supported program types:

```python from qbraid import ConversionGraph

Loads native conversions from QPROGRAM_REGISTRY

graph = ConversionGraph()

graph.plot(legend=True) ```

You can use the native conversions supported by qBraid, or define your own. For example:

```python from unittest.mock import Mock

from qbraid import Conversion, registerprogramtype

replace with any program type

registerprogramtype(Mock, alias="mock")

replace with your custom conversion function

exampleqasm3tomockfunc = lambda x: x

conversion = Conversion("qasm3", "mock", exampleqasm3tomockfunc)

graph.add_conversion(conversion)

using a seed is helpful to ensure reproducibility

graph.plot(seed=20, k=3, legend=True) ```

QbraidProvider

Run experiments using on-demand simulators provided by qBraid. Retrieve a list of available devices:

```python from qbraid import QbraidProvider

provider = QbraidProvider() devices = provider.get_devices() ```

Or, instantiate a known device by ID and submit quantum jobs from any supported program type:

```python device = provider.getdevice("qbraidqirsimulator") jobs = device.run([qiskitcircuit, braketcircuit, cirqcircuit, qasm3_str], shots=1000)

results = [job.result() for job in jobs] batchcounts = [result.data.getcounts() for result in results]

print(batch_counts[0])

{'00': 483, '01': 14, '10': 486, '11': 17}

```

And visualize the results:

```python from qbraid.visualization import plotdistribution, plothistogram

plotdistribution(batchcounts)

plothistogram(batchcounts) ```

Get Involved

• Interested in contributing code, or making a PR? See CONTRIBUTING.md
• For feature requests and bug reports: Submit an issue
• For discussions, and specific questions about the qBraid-SDK join our discord community
• For questions that are more suited for a forum, post to QCSE with the qbraid tag.

Launch on qBraid

The "Launch on qBraid" button (top) can be added to any public GitHub repository. Clicking on it automatically opens qBraid Lab, and performs a git clone of the project repo into your account's home directory. Copy the code below, and replace YOUR-USERNAME and YOUR-REPOSITORY with your GitHub info.

Use the badge in your project's README.md:

markdown [<img src="https://qbraid-static.s3.amazonaws.com/logos/Launch_on_qBraid_white.png" width="150">](https://account.qbraid.com?gitHubUrl=https://github.com/YOUR-USERNAME/YOUR-REPOSITORY.git)

Use the badge in your project's README.rst:

rst .. image:: https://qbraid-static.s3.amazonaws.com/logos/Launch_on_qBraid_white.png :target: https://account.qbraid.com?gitHubUrl=https://github.com/YOUR-USERNAME/YOUR-REPOSITORY.git :width: 150px

License

GNU General Public License v3.0