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Quantum Machine Learning Quantum Simulation

Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework

arXiv

Authors: Syed Farhan Ahmad, Gregory T. Byrd

Year

2026

Paper ID

68020

Status

Preprint

Abstract Read

~2 min

Abstract Words

173

Citations

N/A

Abstract

Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially $mathcal{O}(2ⁿ), and global cost functions suffer from barren plateaus where gradient variance decays exponentially $\mathcal{O}(1/2^n$). This paper introduces and evaluates the Quantum Algorithm for Distributed Reduction of Entanglements (QADR), a hybrid quantum-classical machine learning framework that decomposes a globaln-qubit VQC into localized sub-circuits operating approximately within the causal light cones of individual target qubits. QADR reduces classical simulation memory scaling from\mathcal{O}2ⁿto\mathcal{O}n cdot 2^2d+1for a light cone radiusd$, while naturally mitigating global barren plateaus. We benchmark QADR against standard global VQCs, Support Vector Machines (SVM), and two customized classical parameter-matched neural networks (CANN and PMNN) on the MNIST dataset and the high-dimensional NASA IMS wind turbine drivetrain diagnostic task. QADR demonstrates excellent scalability, operating successfully at n_features=2000 where standard global VQCs crash due to memory exhaustion, while matching or exceeding the performance of optimized classical architectures.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2606.01291 [2] arXiv https://arxiv.org/abs/2606.01291 [3] Publisher https://arxiv.org/abs/2606.01291

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