Parallel Data Processing in Quantum Machine Learning

TL;DR

This paper introduces a quantum machine learning framework that uses quantum parallelism to process entire datasets simultaneously, significantly reducing training complexity and maintaining high classification accuracy.

Contribution

It presents a novel quantum parallelism approach that embeds all training samples into a superposition, lowering loss evaluation complexity from quadratic to linear in dataset size.

Findings

Achieves comparable classification accuracy to traditional methods.

Reduces loss function evaluation complexity from O(N^2) to O(N).

Demonstrates substantial training time savings in simulations.

Abstract

We propose a Quantum Machine Learning (QML) framework that leverages quantum parallelism to process entire training datasets in a single quantum operation, addressing the computational bottleneck of sequential data processing in both classical and quantum settings. Building on the structural analogy between feature extraction in foundational quantum algorithms and parameter optimization in QML, we embed a standard parameterized quantum circuit into an integrated architecture that encodes all training samples into a quantum superposition and applies classification in parallel. This approach reduces the theoretical complexity of loss function evaluation from $O(N^2)$ in conventional QML training to $O(N)$, where $N$ is the dataset size. Numerical simulations on multiple binary and multi-class classification datasets demonstrate that our method achieves classification accuracies comparable to conventional circuits while offering substantial training time savings. These results highlight the potential of quantum-parallel data processing as a scalable pathway to efficient QML implementations.