Quantum Convolution

Quantum convolution involves adapting the convolutional neural network (CNN) architecture for quantum computing, aiming to leverage quantum properties for improved efficiency and performance in various machine learning tasks. Current research focuses on developing quantum convolutional layers with enhanced interpretability and efficiency, exploring different ansatz and architectures within variational quantum circuits, and integrating quantum convolutions into hybrid classical-quantum models. This field holds significant promise for accelerating machine learning applications, particularly in areas like image classification, time series forecasting, and object detection, where it may offer advantages in terms of data efficiency and computational speed.

Papers

May 22, 2024

Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification
Yixiong Chen, Weichuan Fang
Text Classification Quantum Machine Learning Quantum Convolutional Neural Network Quantum Convolution

April 26, 2024

Quantum Adjoint Convolutional Layers for Effective Data Representation
Ren-Xin Zhao, Shi Wang, Yaonan Wang
Quantum Convolutional Neural Network Quantum Convolution Quantum Computer Vision

April 23, 2024

Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting
Sandra Leticia Juárez Osorio, Mayra Alejandra Rivera Ruiz, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Time Series Forecasting Quantum Circuit Variational Quantum Circuit Quantum Convolutional Neural Network Quantum Convolution

April 19, 2024

On-board classification of underwater images using hybrid classical-quantum CNN based method
Sreeraj Rajan Warrier, D Sri Harshavardhan Reddy, Sriya Bada, Rohith Achampeta, Sebastian Uppapalli, Jayasri Dontabhaktuni
Quantum Machine Learning Practical Method Underwater Image Autonomous Underwater Vehicle Underwater Object Detection Hybrid Quantum Classical Quantum Convolution

December 28, 2023

Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications
Hankyul Baek, Donghyeon Kim, Joongheon Kim
Object Detection Quantum Computing Autonomous Driving Application Quantum Convolutional Neural Network Quantum Convolution

July 20, 2023

QDC: Quantum Diffusion Convolution Kernels on Graphs
Thomas Markovich
Graph Drawing Convolution Kernel Convolution Technique Generalized Diffusion Quantum Convolution

November 2, 2022

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, Chin-Hui Lee
Acoustic Feature Acoustic Model Quantum Kernel Acoustic Modeling Command Recognition Quantum Convolution Kernel Classification

August 31, 2022

Classical-to-quantum convolutional neural network transfer learning
Juhyeon Kim, Joonsuk Huh, Daniel K. Park
Quantum Circuit Quantum Convolutional Neural Network Quantum Transfer Learning Imagenet Transfer Quantum Convolution Quantum Classical Convolutional Neural Network

June 8, 2022

Predict better with less training data using a QNN
Barry D. Reese, Marek Kowalik, Christian Metzl, Christian Bauckhage, Eldar Sultanow
Training Data Quantum Advantage Quantum Convolution

Quantum Convolution

Papers

Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification

Quantum Adjoint Convolutional Layers for Effective Data Representation

Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting

On-board classification of underwater images using hybrid classical-quantum CNN based method

Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications

QDC: Quantum Diffusion Convolution Kernels on Graphs

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

Classical-to-quantum convolutional neural network transfer learning

Predict better with less training data using a QNN