MNIST数据库
量子
量子位元
卷积神经网络
量子算法
量子电路
计算机科学
量子纠错
量子傅里叶变换
量子相位估计算法
量子计算机
量子门
量子网络
拓扑(电路)
算法
数学
人工智能
深度学习
物理
量子力学
组合数学
作者
Emmanuel Ovalle-Magallanes,Dora E. Alvarado-Carrillo,Juan Gabriel Aviña-Cervantes,Ivan Cruz–Aceves,José Ruiz-Pinales
标识
DOI:10.1016/j.asoc.2023.110307
摘要
Quantum Machine Learning (QML) has experienced rapid progress in recent years due to the development of Noisy Intermediate-Scale Quantum (NISQ) devices and quantum simulators. Two key elements must be minimized To maintain acceptable computational complexity in QML: the number of qubits required to encode classical data and the number of quantum gates. This paper proposes a novel angle encoding with learnable rotation to drastically reduce the qubits and circuit depth from O(N) to O(⌈log2(N)⌉) qubits, and only N parameterized gates, where N is the input size. Additionally, an extended quantum convolutional layer is introduced with multiple quantum circuits (quantum kernel) that allow for the configuration of any arbitrary size, stride, and dilation analogous to a classical convolutional layer. The proposed quantum convolutional layer learns multiple feature maps with a single quantum kernel while reducing computational cost by employing angle encoding with learnable rotation. Extensive experiments were performed by comparing diverse types of quantum convolutional configurations in a Quantum Convolutional Neural Network (QCNN) over a balanced subset of the MNIST and Fashion-MNIST datasets, achieving an accuracy of 0.90 and 0.7850, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI