# mahout

**Repository Path**: mirrors_apache/mahout

## Basic Information

- **Project Name**: mahout
- **Description**: Mirror of Apache Mahout
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-22
- **Last Updated**: 2026-01-31

## Categories & Tags

**Categories**: Uncategorized

**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)
[![Python](https://img.shields.io/badge/Python-3.10--3.12-blue.svg)](https://www.python.org/)
[![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](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.

## Quick Start

```bash
git clone https://github.com/apache/mahout.git
cd mahout
pip install uv
uv sync                     # Core Qumat
uv sync --extra qdp         # With QDP (requires CUDA GPU)
```

### 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")
```

## Roadmap

### 2024
- [x] Transition of Classic to maintenance mode
- [x] Integration of Qumat with hardened (tests, docs, CI/CD) Cirq, Qiskit, and Braket backends
- [x] Integration with Amazon Braket
- [x] [Public talk about Qumat](https://2024.fossy.us/schedule/presentation/265/)

### 2025
- [x] [FOSDEM talk](https://fosdem.org/2025/schedule/event/fosdem-2025-5298-introducing-qumat-an-apache-mahout-joint-/)
- [x] QDP: Foundation & Infrastructure (Rust workspace, build configuration)
- [x] QDP: Core Implementation (CUDA kernels, CPU preprocessing, GPU memory management)
- [x] QDP: Zero-copy and Safety (DLManagedTensor, DLPack structures)
- [x] QDP: Python Binding (PyO3 wrapping, DLPack protocol)

### Q1 2026
- [ ] QDP: Input Format Support (PyTorch, NumPy, TensorFlow integration)
- [ ] QDP: Verification and Testing (device testing, benchmarking)
- [ ] QDP: Additional Encoders (angle/basis encoding, multi-GPU optimization)
- [ ] QDP: Integration & Release (documentation, example notebooks, PyPI publishing)

## Legal
Please see the `NOTICE.txt` included in this directory for more information.