Release Notes

Quantum Rings SDK v0.12.2

6/23/26

  • OpenQASM 3.x support

    • Control loops and mid-circuit measurements

    • iSWAP gate support

  • Peak memory size determination via Result.get_peakmemorysize()

  • Fidelity measurement via Result.get_fidelity()

  • Machine accuracy in single precision CPU mode

Full Release Notes

Quantum Rings SDK v0.11.2

12/4/25

  • New hybrid serin_quantum_rings engine for improved CPU–GPU efficiency

  • Single and double precision arithmetic options

  • NVIDIA GB10 Superchip support

  • Final measurements now execute in multiple threads; configure via max_threads parameter in run

  • Multi-GPU support

  • Circuit viewer now prints global phase

  • AVX2 instruction set support enabled by default on Linux

  • New AddClbits API on QuantumCircuit for adding classical bits after circuit construction

  • Python 3.11–3.14 support

  • CUDA Toolkit 13.x support

To install (GPU):

# CUDA 12.x
pip install quantumrings[cuda12x]

# CUDA 13.x
pip install quantumrings[cuda13x]

To install (CPU):

pip install quantumrings[cpu]

Full Release Notes

Qiskit Toolkit v0.2.0

12/4/25

  • Full Qiskit 2.x compatibility

  • Auto-transpilation support

  • New advanced primitives: QrStatevectorSampler and QrEstimator

To install (Qiskit 2.x):

pip install quantumrings-toolkit-qiskit

To install (Qiskit 1.x — 1.4.5 or later):

pip install quantumrings-toolkit-qiskit==0.1.20

Full Release Notes

Quantum Rings SDK v0.10

5/30/25

  • NVIDIA GPU support via the amber_quantum_rings backend (Windows and Linux); CPU users can use the scarlet_quantum_rings backend

  • QASM2 importer now supports conditional if statements with indexed classical registers

  • Relaxed append API for greater flexibility when combining circuits

  • measure_all and measure_active now auto-create classical bits if not previously defined

  • QASM2 importer no longer creates classical bits implicitly — use measure_all() or define them explicitly

  • Configuration file can now store backend name for automatic loading via QuantumRingsProvider.get_backend

  • Bug fixes:

    • run method now accepts positional arguments without parameter names

    • QASM2 files with problematic line feed characters now supported

    • inverse API now correctly inverts t and s family gates

    • Fixed slowdown when acquiring the backend multiple times in the same Python instance

    • Fixed qubit label printing in the circuit viewer on some Linux installations

    • c and e now correctly accepted as QuantumRegister names during QASM2 import

To install (CPU):

pip install -i QuantumRingsLib

To install (GPU):

pip install -i quantumrings-nvidia-gpu

Full Release Notes

Quantum Rings for NVIDIA CUDA-Q

3/20/25

  • Quantum Rings simulation technology integrated with NVIDIA CUDA-Q

  • GPU-accelerated quantum circuit simulation available to researchers, developers, and enterprises

  • Run on consumer-grade GPUs for rapid iteration or scale to HPC clusters for advanced workloads

Full Article

Quantum Rings SDK on qBraid

1/30/25

  • Quantum Rings SDK now available natively on qBraid

  • Select the pre-configured Quantum Rings environment and start running simulations immediately

To get started:

  1. Log into qBraid (or sign up)

  2. Select “Launch Lab” to open the Jupyter notebook environment

  3. Under “ENVS” in the right-hand toolbar, search for “Quantum Rings” and select “Add”

  4. Load your script or start coding in a Jupyter notebook

Full Article

Quantum Rings SDK v0.9

12/19/24

  • Expanded documentation with runnable examples for qiskit-optimization and qiskit-machine-learning

Full Release Notes

Qiskit Toolkit v0.1.8

12/19/24

  • Qiskit 1.3 compatibility

  • Support for qiskit-optimization and qiskit-machine-learning

Full Release Notes

Quantum Rings SDK v0.8

12/2/24

  • New Quantum Rings Toolkit for Qiskit

  • Scale quantum circuits without leaving the Qiskit ecosystem

  • Sampler and Estimator runtime classes fully compatible with existing Qiskit-based workflows

  • Compatible with qiskit-finance and qiskit-nature

To install the toolkit:

pip install quantumrings-toolkit-qiskit

To update:

pip install QuantumRingsLib --upgrade

Full Release Notes

Quantum Rings SDK v0.7

10/23/24

  • macOS support

  • License config file support — store your SDK key so it doesn’t need to be included in every script

    • Windows: %APPDATA%\quantumrings\quantumrings.conf

    • Linux & macOS: ~/.config/quantumrings/quantumrings.conf

  • QASM parser now supports custom gates defined and reused within other custom gates

  • QASM function names now support underscores

  • Telemetry moved off the main execution thread to reduce performance impact

  • Bug fix: empty circuits no longer cause unintended behavior

Full Release Notes

Quantum Rings SDK v0.5

6/3/24

  • Google Colab support — run the SDK directly from the cloud with no local setup required

  • Parametric circuit support for dynamic parameter definition and manipulation

  • QASM2 import module now supports include statements

  • New VQE example demonstrating parametric circuits in a hybrid quantum-classical algorithm

  • New self-service Developer Portal for managing license keys, requesting higher qubit limits, and viewing circuit execution history

Full Release Notes

Quantum Rings SDK v0.4

4/23/24

  • Linux support added for Ubuntu, Debian, OpenSUSE Tumbleweed, and Oracle Linux 9.3 — enables cloud computing environments for scaling compute and memory on larger, more complex circuits

  • Python 3.11 compatibility; Python 3.11 is now required

  • Beta users can extend the logical qubit limit to 100 qubits — contact us with your use case to request a custom key

Full Release Notes

Quantum Rings SDK v0.3

3/20/24

  • Substantial optimizations to circuit execution for faster, more efficient quantum computing tasks

  • Measurement data can now be stored in an external file when executing many shots; the first 10,000 shots are automatically stored in RAM. Specify the file via the run method of BackendV2

  • Circuits can now be executed synchronously or asynchronously via the new mode parameter in the run method

  • New performance parameter in run with options HighestEfficiency, BalancedAccuracy, HighestAccuracy, and Automatic (default); default dynamically selects based on circuit complexity and system memory

  • New quiet parameter in run suppresses output by default; set to False to enable progress output and gate-level execution details — useful for long circuits like Sycamore

  • New OptimizeQuantumCircuit module applying nearest-neighbor coupling optimization — lossless and reduces execution time without affecting results

  • Updated documentation for executing Sycamore circuits with quick-reference guidance on the new features

Full Release Notes

For the full news archive, visit quantumrings.com/news.