Quantum Machine Learning
77 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Quantum Machine Learning
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Libraries
Use these libraries to find Quantum Machine Learning models and implementations
iris
Latest papers
Training robust and generalizable quantum models
We derive tailored, parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against perturbations in the input data.
sQUlearn $\unicode{x2013}$ A Python Library for Quantum Machine Learning
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.
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms.
Learning Quantum Processes with Quantum Statistical Queries
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.
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs.
Sub-universal variational circuits for combinatorial optimization problems
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research.
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples
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%.
Neural Networks for Programming Quantum Annealers
boschsamuel/nnforprogrammingquantumannealers
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We explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer.
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
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.
