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Apache Mahout - an environment for quickly creating scalable, performant machine learning applications.
https://mahout.apache.org/
- Rust 51.1%
- Python 37.3%
- Cuda 4.5%
- TypeScript 3.3%
- Jupyter Notebook 1.4%
- Other 2.4%
| Filename | Latest commit message | Latest commit date |
|---|---|---|
| .devcontainer | ||
| .github | ||
| dev | ||
| docs | ||
| examples | ||
| qdp | ||
| qumat | ||
| testing | ||
| website | ||
| .asf.yaml | ||
| .cherry_picker.toml | ||
| .dockerignore | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| CONTRIBUTING.md | ||
| doap_Mahout.rdf | ||
| KEYS | ||
| LICENSE | ||
| lychee.toml | ||
| Makefile | ||
| NOTICE | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
Apache Mahout
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
Qumat
Qumat is a high-level Python library for quantum computing that provides:
- 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 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.
Quick Start
pip install qumat
with QDP (Quantum Data Plane) support
pip install qumat[qdp]
QDP (the qdp extra) requires Python 3.10-3.12 and is not available on Python 3.13.
Qumat: Run a Quantum Circuit
from qumat import QuMat
qumat = QuMat({"backend_name": "qiskit", "backend_options": {"simulator_type": "aer_simulator"}})
qumat.create_empty_circuit(num_qubits=2)
qumat.apply_hadamard_gate(0)
qumat.apply_cnot_gate(0, 1)
qumat.execute_circuit()
QDP: Encode data for Quantum ML
import qumat.qdp as qdp
engine = qdp.QdpEngine(device_id=0)
qtensor = engine.encode([1.0, 2.0, 3.0, 4.0], num_qubits=2, encoding_method="amplitude")
Legal
Please see the NOTICE.txt included in this directory for more information.