Quantum Machine Learning
88 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
Generative quantum machine learning via denoising diffusion probabilistic models
Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data.
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.
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
chenghongz/lim_learning_state
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NeurIPS 2023
As a quantum analog of probability distribution learning, quantum state learning is theoretically and practically essential in quantum machine learning.
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.
Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers.
