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Apache Mahout

Apache Mahout is an open-source framework for building scalable machine learning applications. The project has evolved to include Qumat, a unified Python API for building quantum circuits that runs across multiple quantum backends including Qiskit, Cirq, and Amazon Braket, along with QDP for GPU-accelerated classical-to-quantum data encoding.

2 APIs 8 Features
Distributed ComputingMachine LearningPythonQuantum ComputingScala

APIs

Qumat

Qumat is a unified Python API for building and executing quantum circuits across multiple quantum computing backends including Qiskit, Cirq, and Amazon Braket. It provides a har...

Apache Mahout Samsara

Mahout Samsara is a distributed linear algebra DSL in Scala for building machine learning algorithms on Apache Spark. It provides matrix decompositions, collaborative filtering,...

Features

Hardware-Agnostic Quantum API

Qumat provides a unified API that runs the same quantum circuit code on Qiskit, Cirq, and Amazon Braket backends without modification.

Quantum Gate Operations

Complete library of single-qubit gates (H, X, Y, Z, T, Rx, Ry, Rz, U) and multi-qubit gates (CNOT, Toffoli, SWAP, CSWAP).

Parameterized Quantum Circuits

Support for symbolic parameters in rotation gates for variational quantum algorithms and quantum machine learning.

GPU-Accelerated Data Encoding

QDP provides zero-copy tensor transfer for encoding classical data into quantum states with GPU acceleration.

Distributed Linear Algebra

Samsara DSL enables large-scale matrix operations distributed across Apache Spark clusters.

Collaborative Filtering

Distributed recommendation algorithms including ALS-based collaborative filtering for large-scale datasets.

Clustering

Distributed K-Means, fuzzy K-Means, and spectral clustering algorithms running on Spark.

Dimensionality Reduction

Distributed SVD, PCA, and random projection methods for large-scale feature reduction.

Use Cases

Quantum Machine Learning

Build variational quantum algorithms and quantum neural networks using parameterized circuits via the Qumat API.

Quantum Algorithm Research

Prototype and test quantum algorithms across different hardware backends without rewriting circuit code.

Large-Scale Recommendation

Build distributed recommendation systems processing billions of user-item interactions using Mahout on Spark.

Distributed Clustering

Cluster large datasets using distributed K-Means and other algorithms running on Apache Spark.

Integrations

Qiskit

IBM Qiskit quantum computing framework as a Qumat execution backend for IBM quantum hardware and simulators.

Cirq

Google Cirq quantum computing framework as a Qumat execution backend for Google quantum hardware.

Amazon Braket

AWS Braket quantum computing service as a Qumat execution backend for cloud quantum hardware.

Apache Spark

Primary distributed computing backend for Mahout Samsara linear algebra and machine learning algorithms.

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