Toolkit for Qiskit

The Quantum Rings toolkit for Qiskit lets you run Qiskit circuits and Qiskit ecosystem workflows (Finance, Nature, Optimization, etc.) on Quantum Rings backends.

If you are writing SDK-native code (QuantumRingsLib circuits and primitives), start with the Core SDK: Circuits and Core SDK Examples.

Compatibility requirements

Supported Qiskit version

The Quantum Rings toolkit for Qiskit supports Qiskit 1.4.5 or later. Earlier versions are not supported.

Check your Qiskit version:

import qiskit
print(qiskit.__version__)

Note

Some Qiskit Nature workflows depend on PySCF, which is supported on Linux and macOS but not natively on Windows (use WSL2).

Quantum Rings SDK version

The toolkit requires QuantumRingsLib 0.9.0 or later.

Check your installed version:

import QuantumRingsLib
print(QuantumRingsLib.__version__)

Install the toolkit

This page covers only the toolkit installation. First install the SDK:

Then install the toolkit package:

Qiskit 2.x users

pip install quantumrings-toolkit-qiskit

Qiskit 1.x users

If you are using Qiskit 1.x, install the pinned 0.1.x toolkit version:

pip install quantumrings-toolkit-qiskit==0.1.20

Credentials

The toolkit uses your Quantum Rings credentials to access backends.

Set up credentials using:

Quick verification

This minimal test confirms you can:

  • create a Qiskit circuit

  • acquire a Quantum Rings backend via the toolkit

  • execute the circuit and fetch counts

from qiskit import QuantumCircuit
from quantumrings.toolkit.qiskit import QrRuntimeService

# Acquire the runtime service (token/name shown explicitly here).
# For safer credential handling, save credentials locally (see start/credentials).
service = QrRuntimeService(token="<YOUR_TOKEN_HERE>", name="<YOUR_ACCOUNT_NAME_HERE>")

# CPU-friendly default backend:
backend = service.backend(name="scarlet_quantum_rings", num_qubits=2)

# Bell circuit (Qiskit)
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

job = backend.run(qc, shots=1000, mode="sync")
result = job.result()
print(result.get_counts())

Note

Backend naming depends on your installation and workload. Common backends are: scarlet_quantum_rings (CPU), amber_quantum_rings (GPU), and serin_quantum_rings (hybrid). See Backends.

How backend acquisition works

The toolkit supports multiple patterns for acquiring a Quantum Rings backend for Qiskit workflows:

  • QrRuntimeService (service-style backend acquisition)

  • QrBackendV2 (direct backend construction)

Most users should start with QrRuntimeService because it also supports saving/loading accounts and listing available backends.

Sampler and Estimator guidance

Use V2 of the Sampler and Estimator classes.

Important note about Sampler outputs

The Sampler class does not support quasi-distributions. If you need quasi-distributions, you must compute them yourself (a reference approach is available in the Finance toolkit examples).

Examples and notebooks

API reference