Quantum Machine Learning
416 papers for year 2023
Quantum Machine Learning Research Context
This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.
Showing 37-48 of 416
A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets
Wenjie Liu, Peipei Gao, Yuxiang Wang, Wenbin Yu, Maojun Zhang
Advances in Quantum Computing.
La Cour B.
Advantage of Quantum Machine Learning from General Computational Advantages
Hayata Yamasaki, Natsuto Isogai, Mio Murao
Advantages of quantum communication revealed by the reexamination of hyperbit theory limitations
Giovanni Scala, Seyed Arash Ghoreishi, Marcin Pawłowski
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection
Biswaraj Baral, Reek Majumdar, Bhavika Bhalgamiya, Taposh Dutta Roy
AKQ: A Hybrid Quantum-Classical Image Encryption System
Tran Khanh Nguyen, Nguyen Thi Thanh Truc, Vu Tuan Hai
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
Richard D. P. East, Guillermo Alonso-Linaje, Chae-Yeun Park
An Efficient Quantum Factoring Algorithm
Oded Regev
An Exponential Reduction in Training Data Sizes for Machine Learning Derived Entanglement Witnesses
Aiden R. Rosebush, Alexander C. B. Greenwood, Brian T. Kirby, Li Qian
An improved two-threshold quantum segmentation algorithm for NEQR image
Lu Wang, Zhiliang Deng, Wenjie Liu
An inductive bias from quantum mechanics: learning order effects with non-commuting measurements
Kaitlin Gili, Guillermo Alonso, Maria Schuld
Anatomy of the eigenstates distribution: a quest for a genuine multifractality
Anton Kutlin, Ivan M. Khaymovich