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Found 7 papers, 4 papers with code
Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis.
Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm.
Hierarchical quantum circuit representations for neural architecture search
The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs).
Classical-to-quantum convolutional neural network transfer learning
We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data.
Variational Quantum Approximate Support Vector Machine with Inference Transfer
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data.
A divide-and-conquer algorithm for quantum state preparation
Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space.
Quantum classifier with tailored quantum kernel
Kernel methods have a wide spectrum of applications in machine learning.
Quantum Physics
