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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/chenmiaomiao/eca

공식 코드

Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components

https://github.com/chirag-w/qpsq-learning

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Learning Quantum Processes with Quantum Statistical Queries

https://github.com/chris-n-self/large-scale-qml

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Quantum machine learning of large datasets using randomized measurements

https://github.com/ctglab/QKomics

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Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data

https://github.com/cyx617/Text-Classification-MSFF-QDConv-Model

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

https://github.com/dan-lb/integrated_encoding_for_QuanvNN

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Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks

https://github.com/daniel-fink-de/training-robust-and-generalizable-quantum-models

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Training robust and generalizable quantum models

https://github.com/deanbrand/QSNN

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A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network

https://github.com/dfki-ric-quantum/qdrl-turtlebot-env

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Quantum Deep Reinforcement Learning for Robot Navigation Tasks

https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval

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Quantum Deep Reinforcement Learning for Robot Navigation Tasks

https://github.com/diegoalvareze/qkt_benchmarking

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Benchmarking quantum machine learning kernel training for classification tasks

https://github.com/eperrier/k-p_qnn

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K-P Quantum Neural Networks

https://github.com/francis-hsu/quantgenmdl

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Generative quantum machine learning via denoising diffusion probabilistic models

https://github.com/glivan/tensor_networks_for_probabilistic_modeling

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Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning

https://github.com/gphehub/rbe2308

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Training quantum machine learning models on cloud without uploading the data

https://github.com/guikaiwen/qsp_paper_artifact

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Spacetime-Efficient Low-Depth Quantum State Preparation with Applications

https://github.com/haimengzhao/bounded-gate-tomography

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Learning quantum states and unitaries of bounded gate complexity

https://github.com/hajimesuzuki999/qpf-bic

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Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples

https://github.com/hanzheng98/sn-cqa

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Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze

https://github.com/iitis/RL_for_VQSD_ansatz_optimization

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Enhancing variational quantum state diagonalization using reinforcement learning techniques

https://github.com/ingenii-solutions/ingenii-quantum-hybrid-networks

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A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

https://github.com/jasontslxd/pqwgan

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Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation

https://github.com/jindi0/sqnn

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Scalable Quantum Neural Networks for Classification

https://github.com/jmkuebler/quantumbias

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The Inductive Bias of Quantum Kernels

https://github.com/kclip/noise-aware-loccnet

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Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications

https://github.com/kclip/quantum-cp

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Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning

https://github.com/kezmcd1903/equivariant_qnns

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Reverse Map Projections as Equivariant Quantum Embeddings

https://github.com/konar1987/tr-qnet

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Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks

https://github.com/linuzj/quantum-code-generation

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Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation

https://github.com/lirandepira/paoa-simulations

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Sub-universal variational circuits for combinatorial optimization problems

https://github.com/lockwo/rl_qvc_opt

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Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

https://github.com/louisanity/photonicquantumtrain

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Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing

https://github.com/lsschmid/ising-learning-model

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A general learning scheme for classical and quantum Ising machines

https://github.com/lucasfriedrich97/restricting_to_the_chip_architecture_maintains_the_quantum_neural_network_accuracy

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Restricting to the chip architecture maintains the quantum neural network accuracy

https://github.com/lucasponteslpa/QOCClassifier

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Quantum One-class Classification With a Distance-based Classifier

https://github.com/maiyuren/quantum-random-forest

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A kernel-based quantum random forest for improved classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.