---
# System prepended metadata

title: The Quantum Data Plane - Proposal

---

# Proposal: The Quantum Renaissance of Apache Mahout

Date: November 24, 2025
Subject: A Strategic Change to Quantum Data Infrastructure
Author: Kuan-Hao Huang
> We propose a fundamental strategic pivot for Apache Mahout: transforming it from a legacy machine learning library into the industry-standard data infrastructure for quantum computing.


### Introduction: The Elephant in the Room

Let us address the elephant in the room: Apache Mahout. We are the project that taught Hadoop clusters how to run recommendation systems back when "Big Data" was the buzziest word in Silicon Valley.

You might assume that Mahout has gracefully retired to a digital museum, especially now that we live in the age of Large Language Models. To be honest, we were enjoying a quiet retirement. However, we recently realized something rather startling: Quantum Computing, at its core, is simply linear algebra at a massive scale.

And if there is one thing Mahout knows how to do, it is massive-scale linear algebra.

Therefore, we are cancelling our retirement. We are launching a "Quantum Renaissance." This time, however, we are trading our Java-based walking sticks for jetpacks powered by Rust and CUDA.

### The Problem: Driving a Ferrari in Traffic

Current Quantum Machine Learning (QML) workflows feel a bit like owning a Ferrari but using it to deliver pizzas in rush hour traffic.

The industry possesses incredible simulators and brilliant algorithms. Yet, researchers cannot feed data into them fast enough.

The Status Quo: The Hiking Problem

Imagine you have 1TB of financial data to load into a quantum simulator. The current workflow forces you to write a Python loop. For every single data point, you must pretend you are operating a real quantum computer, carefully simulating rotations for every single qubit.

The simulator obediently processes these gates one by one. This is equivalent to climbing a mountain step-by-step to reach the peak, even though you are inside a computer simulation where the laws of physics are optional.

The result is inefficiency. Your expensive GPUs spend the vast majority of their time idling, waiting for the CPU to finish its hike.

### Our Solution: The Helicopter Approach

We believe hiking is overrated when you have a GPU.

We propose Mahout QDP (Quantum Data Plane). Instead of simulating the journey, we simply teleport to the destination.

By leveraging the brute force of modern GPUs, we skip the tedious gate simulation entirely. We calculate the mathematical result of the encoding process and write it directly into the GPU memory. This approach transforms a computational nightmare into a simple memory bandwidth problem.

The expected result is a 50x to 100x speedup compared to traditional workflows.

### Technical Architecture: The Secret Sauce

We are not inventing a new quantum algorithm. We are building a turbocharger for the existing ecosystem.

#### 1. Rust-First Philosophy
We need absolute control over memory layout and safety. We are embracing Rust to ensure our system is safe, runs neck-and-neck with C++, and integrates natively with Apache Arrow.

#### 2. Direct State Preparation
We have hand-written CUDA kernels to perform matrix operations directly on the GPU. We do not simulate circuits; we perform direct injection. This allows us to bypass the overhead that currently bottlenecks quantum simulators.

#### 3. Zero-Copy Integration
This is perhaps the most critical feature. Once your data is transformed into a Quantum State on the GPU, we use the DLPack protocol to hand the memory pointer directly to PyTorch. There is no copying, no formatting, and no waiting. It allows the Quantum Simulator and PyTorch to effectively share a brain.

### The Roadmap

We have a plan to turn this vision into reality:

#### Phase 1: Proof of Concept
We will prove that our Rust and CUDA implementation is indeed 50x faster than the existing Python-based approaches.

#### Phase 2: Minimum Viable Product
We will release a version that the world can easily install via pip, featuring full Python bindings and PyTorch integration.

#### Phase 3: Ecosystem Integration
We aim to become the acceleration plugin for major frameworks like PennyLane and Qiskit. You write the QML; Mahout handles the heavy lifting of data loading.

### Conclusion

If you are a Data Scientist, this means you can finally train quantum models with real Big Data instead of toy datasets. If you are a Systems Engineer, we offer cutting-edge challenges involving Rust, CUDA, and FFI. And if you are a long-time fan of Mahout, we hope it brings a smile to your face to see the elephant fly.

We are looking for contributors to help us solve the most boring, yet most critical bottleneck in Quantum Computing: Input/Output.

We invite the community to join us in this renaissance. Let us stop simulating the path and start being the destination.