Metadata-Version: 2.4
Name: qumat
Version: 0.5.0
Summary: A library for composing quantum machine learning.
Author-email: Apache Mahout <dev@mahout.apache.org>
License-Expression: Apache-2.0
License-File: LICENSE
License-File: NOTICE
Requires-Python: <3.13,>=3.10
Requires-Dist: amazon-braket-sdk<2.0,>=1.108.0
Requires-Dist: cirq<1.6.0,>=1.5.0
Requires-Dist: qiskit-aer<0.18.0,>=0.17.2
Requires-Dist: qiskit<3.0.0,>=2.2.0
Requires-Dist: sympy<2.0,>=1.14.0
Provides-Extra: qdp
Requires-Dist: qumat-qdp; extra == 'qdp'
Description-Content-Type: text/markdown


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# Apache Mahout

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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

  Apache Mahout

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.
