Apache Mahout
The goal of the Apache Mahout™ project is to build an environment for quickly creating scalable, performant machine learning applications.
Today that work centers on quantum machine learning in Python, with two components: Qumat, a library for writing quantum circuits once and running them on any supported backend, and QDP, a GPU-accelerated data plane that turns classical data into quantum states without simulating state-preparation circuits.
Qumat
Qumat is a high-level Python library for quantum computing. Build a circuit with standard and parameterized gates, then execute it on Qiskit, Cirq, or Amazon Braket through one unified API, on simulators or real quantum hardware.
- One API, three backends - Switch between Qiskit, Cirq, and Amazon Braket by changing a config value, not your circuit code
- Standard and parameterized gates - Hadamard, Pauli, CNOT, Toffoli, SWAP, and rotation gates with parameter binding for variational circuits
- Python 3.10+ - Installable from PyPI with
pip install qumat
QDP (Quantum Data Plane)
QDP removes the data-loading bottleneck in quantum machine learning. Instead of simulating a state-preparation circuit, it constructs the state vector directly in GPU memory and hands it to your training or kernel pipeline.
- Six encodings - Amplitude, angle, basis, phase, IQP, and IQP-Z, with the same coverage on every GPU backend
- NVIDIA CUDA and AMD ROCm - Native CUDA kernels and hand-written Triton kernels for ROCm, selectable from the same API
- Zero-copy interop - DLPack handoff to and from PyTorch, NumPy, and TensorFlow, plus GPU-pointer paths that skip the host round trip
- Benchmarked on real workloads - SVHN IQP training, quantum kernel SVM, and data-to-state latency benchmarks ship with the project
Looking for the earlier Mahout Classic (Samsara and MapReduce) codebase? It is in maintenance mode. The community's current work is on Qumat and QDP.