2 posts tagged with "release"

We are excited to announce QuMat v0.6.0, the next release of Mahout's quantum machine learning stack.

The main theme of this release is moving QDP from an NVIDIA-focused proof of concept into a broader GPU data plane for quantum ML. QuMat v0.6.0 brings AMD ROCm support to first-class status, closes encoder parity gaps between CUDA and ROCm, adds realistic benchmark workloads, and refreshes the documentation site for users and contributors.

Special thanks to everyone who contributed to this release. We would like to thank PMC members Jie-Kai Chang, Guan-Ming Chiu, Andrew Musselman, Shannon Quinn (PMC Chair) and Trevor Grant. We also thank Committers Hsien-Cheng Huang, and Kuan-Hao Huang, along with contributors Yu-Ting Hsiung (Microsoft), Guan-Hua Wen (Microsoft), An-Te Tsai (Phind), Chao-Hong Yeh (Trend Micro), Han-Wen Tsao, Yu-Chen Lai and the broader Apache Mahout community.

We are excited to announce QuMat v0.5.0, the next release of Mahout's quantum machine learning stack.

The main theme of this release is moving QuMat from a circuit abstraction into a more complete quantum ML development platform. The release introduces the first public proof-of-concept of QDP (Quantum Data Plane), adds GPU-accelerated data-to-state encoding, improves QuMat's backend behavior, and tightens the development workflow around testing, packaging, and documentation.

Special thanks to everyone who contributed to this release. We would like to thank PMC members Jie-Kai Chang, Guan-Ming Chiu, Andrew Musselman, Shannon Quinn (PMC Chair), and Trevor Grant. We also thank Committers Hsien-Cheng Huang, Kuan-Hao Huang and Krishna Dave, along with contributors Nai-Jui Yeh (Union.ai), Che-Yu Wu (Synology), Jia-Wei Jiang (TSMC), Guan-Hua Wen, Yu-Chen Lai, and the broader Apache Mahout community.