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The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications.
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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]

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")

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

2025

[x] FOSDEM talk
[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)

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Apache Mahout - an environment for quickly creating scalable, performant machine learning applications.