Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication Related

Topics

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

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2026 reference point for readers tracking recent quantum research.
Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector...

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

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

Local Citation Graph (Related-Paper Links)

External citation index: OpenAlex citation signal • updated 2026-10-21 02:56:32

Community Reactions

Quick sentiment from readers on this paper.

Score: 0

Likes: 0 Dislikes: 0

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.