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GitHub topics: quantum-kernel

tilmann-rs/qek-learn

Trainable, scalable quantum embedding kernels with variational parameters

Language: Python - Size: 301 KB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 2 - Forks: 0

anthonyrtw/LevyLieb_VQE

Calculation of the ground state energy of a Hubbard dimer using the Levy-Lieb density functional.

Language: Jupyter Notebook - Size: 2.37 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

themisvaltinos/Quantum-Neural-Networks

Implementations of quantum circuits for Quantum Neural Networks with Qutrits

Language: Jupyter Notebook - Size: 16.8 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 6 - Forks: 1

albertnieto/quantum_convolutional_nn_benchmark

Benchmarking quanvolutional neural networks with QCML and Pennylane.

Language: Jupyter Notebook - Size: 922 KB - Last synced at: 2 months ago - Pushed at: 6 months ago - Stars: 4 - Forks: 1

sergio94al/AutoQML-Quantum-Inspired-Kernels-by-Using-Genetic-Algorithms-for-Grayscale-images

This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for grayscale images, optimizing both quantum circuits and dimensionality reduction method.

Language: Jupyter Notebook - Size: 170 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

qucai-lab/adapt-qka

[Hackathon] Adaptive QKA - QHack2023 24hrs Hackathon - Contain a Supplementary Material

Language: Jupyter Notebook - Size: 320 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

sergio94al/Automatic_design_of_quantum_feature_maps_Genetic_Auto-Generation

Registered Software. Official code of the published article "Automatic design of quantum feature maps". This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for tabular data.

Language: Jupyter Notebook - Size: 2.61 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 7 - Forks: 4

maximer-v/quantum-machine-learning

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

Language: Jupyter Notebook - Size: 644 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 18 - Forks: 5

Related Keywords
quantum-kernel 8 quantum-machine-learning 7 quantum-computing 5 automatic-machine-learning 2 qiskit 2 artificial-intelligence 2 quantum-inspired-algorithm 2 pennylane 2 qml 2 machine-learning 2 genetic-algorithms 2 evolutionary-algorithms 2 quantum-algorithms 2 optimization-methods 1 multiobjective-optimization 1 kernel-methods 1 genetic-algorithm 1 evolutionary-strategy 1 nisq 1 supervised-learning-algorithms 1 qiskit-aqua 1 qiskit-tutorial 1 qml-applications 1 quantum 1 quantum-programming-language 1 support-vector-classifier 1 support-vector-machines 1 synthetic-data 1 variational-quantum-classifier 1 quantum-embedding 1 quantummachinelearning 1 density-functional-theory 1 variational-quantum-eigensolver 1 classification 1 paper 1 pqc 1 quantum-neural-networks 1 qutrits 1 vqa 1 convolutional-neural-networks 1 automl 1 optimization 1 quantum-artificial-intelligence 1 hackathon-project 1 scikit-learn 1 classifiers 1