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Fully convolutional networks are robust in performing semantic segmentation,with many applications from signal processing to computer vision. From thefundamental principles of variational quantum algorithms, we propose a feasiblepure quantum architecture that can be operated on noisy intermediate-scalequantum devices. In this work, a parameterized quantum circuit consisting ofthree layers, convolutional, pooling, and upsampling, is characterized bygenerative one-qubit and two-qubit gates and driven by a classical optimizer.This architecture supplies a solution for realizing the dynamical programmingon a one-way quantum computer and maximally taking advantage of quantumcomputing throughout the calculation. Moreover, our algorithm works on manyphysical platforms, and particularly the upsampling layer can use eitherconventional qubits or multiple-level systems. Through numerical simulations,our study represents the successful training of a pure quantum fullyconvolutional network and discusses advantages by comparing it with the hybridsolution.

Feasible Architecture for Quantum Fully Convolutional Networks