Fully convolutional networks are robust in performing semantic segmentation, with many applications from signal processing to computer vision. From the fundamental principles of variational quantum algorithms, we propose a feasible pure quantum architecture that can be operated on noisy intermediate-scale quantum devices. In this work, a parameterized quantum circuit consisting of three layers, convolutional, pooling, and upsampling, is characterized by generative one-qubit and two-qubit gates and driven by a classical optimizer. This architecture supplies a solution for realizing the dynamical programming on a one-way quantum computer and maximally taking advantage of quantum computing throughout the calculation. Moreover, our algorithm works on many physical platforms, and particularly the upsampling layer can use either conventional qubits or multiple-level systems. Through numerical simulations, our study represents the successful training of a pure quantum fully convolutional network and discusses advantages by comparing it with the hybrid solution.
翻译:完全进化的网络在进行语义分割方面非常强大,从信号处理到计算机视觉等许多应用程序。 根据变异量算法的基本原则,我们提出一个可行的纯量子结构,可以在吵闹的中间级量子装置上运行。在这项工作中,一个由三层组成的参数化量子电路,即革命性、集合和升级,其特征是基因化的单位和双位门,由古典优化器驱动。这一结构为在单向量子计算机上实现动态编程提供了解决方案,并最大限度地在整个计算过程中利用量子计算。此外,我们在许多物理平台上的算法工作,特别是高标层可以使用常规的量子或多层系统。通过数字模拟,我们的研究代表了对纯量的全进化网络的成功培训,并通过将其与混合解决方案进行比较来讨论优势。