Quantum Machine Learning
77 papers with code • 2 benchmarks • 1 datasets
LibrariesUse these libraries to find Quantum Machine Learning models and implementations
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn.
This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms.
Learning complex quantum processes is a central challenge in many areas of quantum computing and quantum machine learning, with applications in quantum benchmarking, cryptanalysis, and variational quantum algorithms.
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs.
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research.
Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98. 9% to 99. 2%, 97. 8% to 98. 3%, and 71. 2% to 76. 1%, respectively, but degraded it against GTSRB from 93. 5% to 92. 0%.
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e. g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection.