QCNN layers equivariant under pixel cyclic shifts are exactly characterized as Fourier-mode multiplexers after QFT, enabling a deep network with constant expected gradient norm at initialization.
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The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
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Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers
QCNN layers equivariant under pixel cyclic shifts are exactly characterized as Fourier-mode multiplexers after QFT, enabling a deep network with constant expected gradient norm at initialization.
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Measurement-based quantum machine learning
The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.