Multi-channel convolutional neural quantum embedding
Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational quantum circuits involves embedding the data into a quantum Hilbert space and optimizing the circuit parameters to train the measurement process. In this context, the efficacy of QSL is inherently influenced by the selection of quantum embedding. In this study, we introduce a classical-quantum hybrid approach for optimizing quantum embedding beyond the limitations of the standard circuit model of quantum computation (i.e., completely positive and trace-preserving maps) for general multi-channel data. We benchmark the performance of various models in our framework using the CIFAR-10 and Tiny ImageNet datasets and provide theoretical analyses that guide model design and optimization.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantum Machine LearningSimilar Papers 제목 키워드 기반
Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly …
Quantum Machine LearningRGB Image Classification with Quantum Convolutional Ansaetze
With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of inte…
Classificationimage-classificationImage ClassificationQuantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement
In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise con…
Quantum Machine LearningQuantum-machine-learning channel discrimination
In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies …
BIG-bench Machine LearningQuantum Machine LearningThe role of data embedding in equivariant quantum convolutional neural networks
Geometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainability and generalization. Recently this ide…
ClassificationQuantum Machine Learning