# Mahout

**Repository Path**: apache/mahout

## Basic Information

- **Project Name**: Mahout
- **Description**: 项目主要目标是创建一些可伸缩的机器学习算法
- **Primary Language**: Java
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 9
- **Forks**: 4
- **Created**: 2019-10-22
- **Last Updated**: 2026-10-01

## Categories & Tags

**Categories**: machine-learning

**Tags**: None

## README


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# Apache Mahout

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
[![PyPI version](https://img.shields.io/pypi/v/qumat.svg?color=blue)](https://pypi.org/project/qumat/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/qumat.svg?color=blue)](https://pypi.org/project/qumat/)
[![GitHub Stars](https://img.shields.io/github/stars/apache/mahout.svg)](https://github.com/apache/mahout/stargazers)
[![GitHub Contributors](https://img.shields.io/github/contributors/apache/mahout.svg)](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

<p align="center">
  <img src="https://raw.githubusercontent.com/apache/mahout/refs/heads/main/docs/assets/mascot_with_text.png" width="400" alt="Apache Mahout">
</p>

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](https://mahout.apache.org/docs/qumat/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

```bash
pip install qumat
```

with QDP (Quantum Data Plane) support

```bash
pip install qumat[qdp]
```

### Qumat: Run a Quantum Circuit

```python
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

```python
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.