CUDA-Q Releases¶
latest
The latest version of CUDA-Q is on the main branch of our GitHub repository and is also available as a Docker image. More information about installing the nightly builds can be found here
0.8.0
The 0.8.0 release adds a range of changes to improve the ease of use and performance with CUDA-Q. The changes listed below highlight some of what we think will be the most useful features and changes to know about. While the listed changes do not capture all of the great contributions, we would like to extend many thanks for every contribution, in particular those from external contributors.
The full change log can be found here.
0.7.1
The 0.7.1 release adds simulator optimizations with significant performance improvements and
extends their functionalities. The nvidia-mgpu backend now supports user customization of the
gate fusion level as controlled by the CUDAQ_MGPU_FUSE environment variable documented
here.
It furthermore adds a range of bug fixes and changes the Python wheel installation instructions.
The full change log can be found here.
0.7.0
The 0.7.0 release adds support for using NVIDIA Quantum Cloud, giving you access to our most powerful GPU-accelerated simulators even if you don’t have an NVIDIA GPU. With 0.7.0, we have furthermore greatly increased expressiveness of the Python and C++ language frontends. Check out our documentation to get started with the new Python syntax support we have added, and follow our blog to learn more about the new setup and its performance benefits.
The full change log can be found here.
0.6.0
The 0.6.0 release contains improved support for various HPC scenarios. We have added a plugin infrastructure for connecting CUDA-Q with an existing MPI installation, and we’ve added a new platform target that distributes workloads across multiple virtual QPUs, each simulated by one or more GPUs.
Starting with 0.6.0, we are now also distributing
pre-built binaries for using CUDA-Q with C++.
The binaries are built against the GNU C library
version 2.28.
We’ve added a detailed Building from Source guide to build these binaries for older glibc versions.
The full change log can be found here.
0.5.0
With 0.5.0 we have added support for quantum kernel execution on OQC and IQM backends. For more information, see CUDA-Q Hardware Backends. CUDA-Q now allows to executing adaptive quantum kernels on quantum hardware backends that support it. The 0.5.0 release furthermore improves the tensor network simulation tools and adds a matrix product state simulator, see CUDA-Q Simulation Backends.
Additionally, we are now publishing images for experimental features, which currently includes improved Python language support. Please take a look at Installation Guide for more information about how to obtain them.
The full change log can be found here.
0.4.1
The 0.4.1 release adds support for ARM processors in the form of multi-platform Docker images and aarch64 Python wheels. Additionally, all GPU-based backends are now included in the Python wheels as well as in the Docker image.
The full change log can be found here.
0.4.0
CUDA-Q is now available on PyPI! The 0.4.0 release adds support for quantum kernel execution on Quantinuum and IonQ backends. For more information, see CUDA-Q Hardware Backends.
The 0.4.0 PyPI release does not yet include all of the GPU-based backends.
The fully featured version is available as a Docker image for linux/amd64 platforms.
The full change log can be found here.
0.3.0
The 0.3.0 release of CUDA-Q is available as a Docker image for linux/amd64 platforms.