HYM-qm-stu
My name is HU yuanming. Master of Integrated Circuit Engineering, Anhui University Research interests: Quantum machine learning
HYM-qm-stu's Stars
JeremyLinux/PyTorch-Radial-Basis-Function-Layer
An implementation of an RBF layer/module using PyTorch.
eugeniashurko/rbfnnpy
Radial Basis Function Neural Network implementation for Python
amyami187/effective_dimension
The power of quantum neural networks
quantum-ai-for-cardiac-imaging/cardiomegaly-chest-x-ray
hybrid classical-quantum transfer learning for cardiomegaly detection on chest x-rays
jindongwang/transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
open-mmlab/mmpretrain
OpenMMLab Pre-training Toolbox and Benchmark
jogisuda/QuantumSentenceTransformer
Quantum-Enhanced Transfer Learning for Natural Language Processing
christorange/QC-CNN
Quantum-classical hybrid convolutional neural network for classical image classification
PennyLaneAI/qml
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
cassiejayne/Quantum_Transfer_Learning
This repository is to document my testing of a code developed to employ quantum transfer learning, demonstrated by a research team at Xanadu.
takh04/QCNN
marcellodebernardi/loss-landscapes
Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch
AccumulateMore/CV
✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】
josephtedds/QuantumDataAugmentation
kssr1345/Qunatum-tranfer-learning-for-diabetic-rethinpathy
India is on track to become the world’s diabetes capital thus demanding accurate diagnosis of Diabetic retinopathy from optical coherence tomography (OCT) retinal images. Accurate and faster diagnosis is difficult as it depends on quality of image, operator handling and also the growing number of patients. In this paper we propose the use of quantum transfer learning model to accomplish diagnosis of Diabetic Retinopathy. Quantum Transfer Learning (QTL), is a hybrid combination of classical transfer learning and quantum computing. Unlike classical computers, quantum computers provide faster computation and better accuracy. The concept of QTL is mainly used where the dataset size is limited. The QTL model, diagnostically significant image features are extracted with Resnet18 Convolutional Neural NEtwork (CNN) model, which is reduced to 4-bit feature vector to be encoded as qubit and is finally classified by utilizing Variational Quantum Circuit (VQC). The proposed model gave a better accuracy than existing state of the art methods in terms of high accuracy despite with a smaller set of images in the training phase.
edu-yinzhaoxia/Defense-against-Adversarial-Attacks-by-Low-level-Image-Transformations
This code is the implementation of the adversarial defense method introduced in the paper "Defense against Adversarial Attacks by Low-level Image Transformations".
sangxia/nips-2017-adversarial
Adversarial Attacks and Defenses of Image Classifiers, NIPS 2017 competition track
Slimane33/QuantumClassifier
An example of a variational quantum classifier implemented with qiskit using only elementary gates.
LWKJJONAK/Quantum_Neural_Network_Classifiers
Codes for efficiently implementing quantum neural network classifiers
ikoloska/Training-Hybrid-Classical-Quantum-Classifiers-via-Stochastic-Variational-Optimization
Code and tutorial for the results reported in "Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization".
Rlag1998/Embedding_Generalization
Exploring the Generalization Performance of Quantum Metric Learning Classifiers
rodneyosodo/variational-quantum-classifier-on-heartattack
Implement variational quantum classifier on heart attack data available on kaggle with the aim to understand different variational models and different feature maps
bagmk/Quantum_Machine_Learning_Express
This project is one of the Qiskit mentorship programs to replicate two papers arXiv:1905.10876 and arXiv:2003.09887 using the Qiskit environment. We evaluate the parameterized quantum circuit, reproduce the expressibility and entangling capability of the 19 circuits, and the classification accuracy.
XanaduAI/quantum-transfer-learning
A transfer learning approach applied to hybrid neural networks composed of classical and quantum elements.
darthsimpus/RQNN
ESA-PhiLab/QNN4EO
philippaltmann/SEQUENT
DicksonWu654/QCNNs
yh08037/quantum-neural-network
Qiskit Hackathon Korea 2021 Community Choice Award Winner : Exploring Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
timqqt/MERA_Image_Classification
MERA tensor network for tiny object image classification