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Quantum Machine Learning
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AutoQML/autoqml
공식 코드https://github.com/Christophe-pere/An-encoding-perspective-for-quantum-classification-advantage-using-NISQ-algorithms
공식 코드https://github.com/ESA-PhiLab/QNN4EO
공식 코드Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
https://github.com/GitsSaikat/QuXAI
공식 코드QuXAI: Explainers for Hybrid Quantum Machine Learning Models
https://github.com/Idsl-group/QuGAP
공식 코드Generating Universal Adversarial Perturbations for Quantum Classifiers
https://github.com/IvanMaksymov/Quantum-Tunnelling-Neural-Networks-Tutorial
공식 코드https://github.com/JonasLandman/quantum_kmeans_NeurIPS_2019
공식 코드q-means: A quantum algorithm for unsupervised machine learning
https://github.com/LSchatzki/NTangled_Datasets
공식 코드https://github.com/LeaMarion/quantum-enhanceable-deep-rl
공식 코드Quantum enhancements for deep reinforcement learning in large spaces
https://github.com/LukePower01/ml-to-qml
공식 코드Feature Importance and Explainability in Quantum Machine Learning
https://github.com/MahdiChehimi/Quantum-Federated-Learning-with-Quantum-Data
공식 코드https://github.com/PaddlePaddle/Quantum
공식 코드Variational quantum Gibbs state preparation with a truncated Taylor series
https://github.com/PennyLaneAI/pennylane
공식 코드PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PlanQK/variational-quanvolutional-neural-networks
공식 코드Variational Quanvolutional Neural Networks with enhanced image encoding
https://github.com/QQQYang/QNN-Baseline
공식 코드https://github.com/Qyusu/astronaut
공식 코드Automating quantum feature map design via large language models
https://github.com/Raijeku/discriminating-quantum-states
공식 코드https://github.com/RaoulHeese/qshaptools
공식 코드Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
https://github.com/Tomev/AQMLator
공식 코드https://github.com/UCL-CCS/PEQAD
공식 코드A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
https://github.com/XanaduAI/expressive_power_of_quantum_models
공식 코드The effect of data encoding on the expressive power of variational quantum machine learning models
https://github.com/alessandropoggiali/qpb-autoencoder
공식 코드https://github.com/anthonysmaldone/qcnn-multi-channel-supervised-learning
공식 코드Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
https://github.com/anupamaray/ensembleqml_application
공식 코드https://github.com/askolik/eqc_for_nco
공식 코드Equivariant quantum circuits for learning on weighted graphs
https://github.com/barnrang/Trainable-Embedding-QML
공식 코드Trainable Discrete Feature Embeddings for Variational Quantum Classifier
https://github.com/bioIntelligence-Lab/qumi
공식 코드https://github.com/boschsamuel/nnforprogrammingquantumannealers
공식 코드https://github.com/bpcarlos/double_descent_quantum_ml
공식 코드https://github.com/bpcarlos/understanding_qml_rethinking_gen
공식 코드Understanding quantum machine learning also requires rethinking generalization
https://github.com/cern-it-innovation/gqc
공식 코드Guided Quantum Compression for High Dimensional Data Classification
https://github.com/cern-it-innovation/quask
공식 코드https://github.com/chacconed/quantum-convolutional-neural-networks-with-interaction-layers-for-classification-of-classical-data
공식 코드Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
https://github.com/chariharasuthan/qultsf
공식 코드QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
https://github.com/chenghongz/lim_learning_state
공식 코드Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.