planqk-quantum-client 1.1.2

Client SDK to run workloads on quantum devices provided by the PLANQK Platform. .

⚠️ DEPRECATED ⚠️

This package has been deprecated and is no longer maintained.

Please migrate to the new package:

• PyPI: qhub-api

Install the new package:

pip install qhub-api

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PLANQK Quantum Client

The PLANQK Quantum Client is a Python SDK for accessing quantum computing backends through the PLANQK platform. It provides a low-level interface for direct interaction with quantum hardware and simulators, offering fine-grained control over job submission, backend management, and result retrieval.

A more detailed documentation of the client can be found here.

Installation

The package is published on PyPI and can be installed via pip (Python 3.11 or higher is required):

pip install --upgrade planqk-quantum-client

Quick Start

Basic Setup

To get started with the PLANQK Quantum Client, you'll need a valid access token from the PLANQK platform. You can obtain this from the PLANQK dashboard.

from planqk.quantum.client import PlanqkQuantumClient

# Initialize the client
client = PlanqkQuantumClient(
    access_token="your_access_token_here"
)

Exploring Backends

# List all available backends
backends = client.backends.get_backends()
for backend in backends:
    print(f"{backend.id} - {backend.name}")

# Get detailed backend information
backend_info = client.backends.get_backend("aws.ionq.aria")
print(f"Backend: {backend_info.name}")
print(f"Qubits: {backend_info.configuration.qubit_count}")
print(f"Technology: {backend_info.technology}")
...

# Check backend status and availability
status = client.backends.get_backend_status("azure.ionq.simulator")
print(f"Status: {status.status}")

Submitting a Quantum Job

This example demonstrates how to submit a quantum circuit using the IonQ native gate format. The circuit applies a Hadamard gate to the first qubit (creating a superposition) and an X gate to the second qubit (flipping it to |1⟩), resulting in the state |+1⟩.

from planqk.quantum.client.sdk import AzureIonqJobInput

# Define a quantum circuit using IonQ format
ionq_input = AzureIonqJobInput(
    gateset="qis",
    qubits=2,
    circuits=[
        {"type": "h", "targets": [0]},
        {"type": "x", "targets": [1], "controls": [0]},
    ]
)

# Submit job to a simulator
job = client.jobs.create_job(
    backend_id="azure.ionq.simulator",
    name="My Quantum Job",
    shots=100,
    input=ionq_input,
    input_params={},
    input_format="IONQ_CIRCUIT_V1"
)

print(f"Job submitted with ID: {job.id}")
print(f"Initial status: {job.status}")

# Wait for completion and get results
job_id = job.id
final_job = client.jobs.get_job(job_id)

if final_job.status == 'COMPLETED':
    results = client.jobs.get_job_result(job_id)
    print(f"Histogram: {results['histogram']}")
    # Expected output: {"00": ~50, "11": ~50} due to H gate on qubit 0 and X gate on qubit 1


elif final_job.status == 'FAILED':
    error = client.jobs.get_job_result(job_id)
    print(f"Job failed: {error}")

Different backends require specific input formats (in the example AzureIonqJobInput). Therefore, the SDK provides backend-specific classes (see documentation for details).

The type of the job result is a dictionary whose structure depends on the backend being used.

Important: The client does not perform transpilation. Input must match the target backend's capabilities and connectivity, including gates, qubits, and other backend-specific requirements.