论文标题
NISQ时代的混合量子图像边缘检测器
A hybrid quantum image edge detector for the NISQ era
论文作者
论文摘要
边缘是图像位置，灰色值强度突然变化。它们是理解和细分图像的最重要功能。边缘检测是数字图像处理中的标准任务，例如使用过滤技术解决。但是，要处理的数据量迅速增长，甚至将超级计算机推到其极限上。与经典位数相比，量子计算有望根据Qubits的数量成倍降低存储器使用情况。在本文中，我们提出了一种基于量子人造神经元的概念的量子边缘检测的混合方法。我们的方法实际上可以在量子计算机上实施，尤其是在当前嘈杂的中间尺度量子时代的计算机上。我们比较了减少电路数的六种变体，从而比较了量子边缘检测所需的时间。利用我们方法的可扩展性，我们实际上可以检测到比以前所触及的要大得多的图像中的边缘。
Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved for example using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.
