Quantum Rings SDK 0.11.2 and Qiskit Toolkit 0.2.0 Release Notes

We're thrilled to roll out two major updates today:

Quantum Rings SDK version 0.11.2 and the all-new Quantum Rings Toolkit for Qiskit 2.x version 0.2.0!

Meanwhile, the Qiskit Toolkit brings seamless integration with Qiskit 2.0, automating transpilation and unlocking advanced primitives like statevector samplers and estimators. Together, these releases make it easier than ever to build, simulate, and scale quantum applications in your existing environments, from standalone circuits to full Qiskit pipelines.

The Core Quantum Rings SDK introduces:

• The innovative new serin_quantum_rings hybrid engine for more efficient CPU-GPU workflows
• Flexible single and double precision arithmetic, for faster than ever simulation to meet all needs
• Native support for the NVIDIA GB10 Superchip
• Expanded compatibility for Python 3.11–3.14 and CUDA 13.x.

The Qiskit Toolkit brings seamless integration with Qiskit 2.0, automating transpilation and unlocking advanced primitives like statevector samplers and estimators.

Together, these releases make it easier than ever to build, simulate, and scale quantum applications in your existing environments, from standalone circuits to full Qiskit pipelines.

Installation Instructions

Uninstall any previous versions, then install the new version using pip install.

Note for CUDA Users]: CUDA Toolkit 12.x or 13.x  is required for the GPU-enabled version. Users can install it by following the instructions found at NVIDIA’s website: [CUDA Toolkit - Free Tools and Training | NVIDIA Developer

Installation Procedure - Core SDK

GPU Version

CUDA 12.x Users:

pip install quantumrings[cuda12x]

CUDA 13.x Users:

pip install quantumrings[cuda13x]

CPU Version

pip install  quantumrings[cpu]

Legacy Installation Commands

CPU Installation:

Installs the latest CPU-Only Quantum Rings SDK

pip install  QuantumRingsLib

Latest CUDA Installation:

Installs the latest GPU-Enabled Quantum Rings SDK, compiled for the most up-to-date version of Cuda:

pip install quantumrings-nvidia-gpu

Installation Procedure - Qiskit SDK

Due to updates between Qiskit 1.x and 2.x, we will now be maintaining multiple versions of the Qiskit Toolkit. When installing, you will need to be sure to choose a version that is appropriate for the version of Qiskit you are running:

Qiskit 2.x

pip install quantumrings-toolkit-qiskit

Qiskit 1.x (1.4.5 or later)

pip install quantumrings-toolkit-qiskit==0.1.20

New Features and Enhancements in this release

• Introduction of a new engine ‘serinquantumrings’ which uses a hybrid mode and runs mainly on the CPU, while the GPU is used in some larger calculations.
• Introduction of ‘single’ and ‘double’ precision arithmetic. Users can select the precision either when selecting the backend or when running the circuit.
• Support for NVIDIA GB10 Superchip added
• The final measurements, if any,  are now done in multiple threads. The maximum number of threads can be set in the run method while submitting the circuit for execution using the parameter ‘max_threads’.
• In configurations with multiple GPUs, the user can select the GPU ID to use when choosing the backend.

• Circuit Viewer now prints the global phase, if any.
• AVX2 instruction set support for the Linux platform is enabled by default.
• Added a new API to the QuantumCircuit class AddClbits to add classical bits after the circuit is constructed.
• Supports Python 3.11, 3.12, 3.13, or 3.14 on all supported platforms.
• Support for CUDA Toolkit 13.x in addition to 12.x.

Known Issues and Limitations

QASM files with user-defined gates containing measure or reset instructions embedded inside those user-defined gates will not import. This will be supported in the future.

NOTICE: Important Changes Coming up soon

When constructing a QuantumCircuit, explicitly specify the number of Classical bits required. If this is not specified, classical bits will not be created.

Example:

qc = QuantumCircuit( 5, 3 ) # create five qubits and three classical bits.