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Quantum CNNs achieve translation equivariance via Fourier multiplexers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new type of quantum convolutional neural network (QCNN) that is designed to be equivariant to pixel shifts, a property crucial for image recognition tasks. This advancement addresses a mismatch in existing QCNNs where translation equivariance is often limited to cyclic permutations of qubits. The proposed QCNN utilizes Fourier transforms to achieve exact pixel cyclic shift equivariance, potentially improving performance on quantum data processing tasks. AI

IMPACT Introduces a new QCNN architecture that could improve image processing capabilities on quantum computers.

RANK_REASON This is a research paper detailing a novel QCNN architecture.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum CNNs achieve translation equivariance via Fourier multiplexers

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This is a research paper detailing a novel QCNN architecture.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dmitry Chirkov, Igor Lobanov ·

    Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers

    arXiv:2604.06094v2 Announce Type: replace-cross Abstract: Convolutional neural networks owe much of their success to hard-coding translation equivariance. Quantum convolutional neural networks (QCNNs) have been proposed as near-term quantum analogues, but the relevant notion of t…

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