AI RESEARCH
Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers
arXiv CS.LG
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ArXi:2604.06094v1 Announce Type: cross 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 translation depends on the data encoding. For address/amplitude encodings such as FRQI, a pixel shift acts as modular addition on an index register, whereas many MERA-inspired QCNNs are equivariant only under cyclic permutations of physical qubits.