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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]

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")

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