ryankert01 commented on code in PR #808: URL: https://github.com/apache/mahout/pull/808#discussion_r2681833049
########## README.md: ########## @@ -16,66 +16,76 @@ See the License for the specific language governing permissions and limitations under the License. --> -Welcome to Apache Mahout! -=========== -The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications. +# Apache Mahout +[](https://www.apache.org/licenses/LICENSE-2.0) +[](https://www.python.org/) +[](https://github.com/apache/mahout/stargazers) +[](https://github.com/apache/mahout/graphs/contributors) + +The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications.\ For additional information about Mahout, visit the [Mahout Home Page](http://mahout.apache.org/) - +## Qumat -# Qumat +<p align="center"> + <img src="docs/assets/mascot_with_text.png" width="400" alt="Apache Mahout"> +</p> -Qumat is a POC of a high level Python library for intefacing with multiple quantum computing backends. It is designed to be easy to use and to abstract the particularities of each backend, so that you may 'write once, run anywhere.' Like the Java of quantum computing, but Java is the new COBOL so we're trying to distance ourselves from that comparison :P +Qumat is a high-level Python library for quantum computing that provides: -Check out [basic gates](docs/basic_gates.md) for a quick introduction to the basic gates. These are now supported across multiple quantum computing frameworks, including Qiskit, Cirq, and Braket. +- **Quantum Circuit Abstraction** - Build quantum circuits with standard gates (Hadamard, CNOT, Pauli, etc.) and run them on Qiskit, Cirq, or Amazon Braket with a single unified API. Write once, execute anywhere. Check out [basic gates](docs/basic_gates.md) for a quick introduction to the basic gates supported across all backends. +- **QDP (Quantum Data Plane)** - Encode classical data into quantum states using GPU-accelerated kernels. Zero-copy tensor transfer via DLPack lets you move data between PyTorch, NumPy, and TensorFlow without overhead. -## Getting started +## Quick Start Review Comment: We can add a git clone mahout. ########## README.md: ########## @@ -16,66 +16,76 @@ See the License for the specific language governing permissions and limitations under the License. --> -Welcome to Apache Mahout! -=========== -The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications. +# Apache Mahout +[](https://www.apache.org/licenses/LICENSE-2.0) +[](https://www.python.org/) Review Comment: We can use dynamic badge from PyPI once we are on PyPI. (dynamic version) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
