Quantum Machine Learning
1,566 papers for year 2026
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 25-36 of 1,566
A Hybrid Quantum Machine Learning Framework for Medical Data Analysis and Disease Diagnosis
Hemalatha Natte, Dolapriya Kasilinka, Suresh Mylapalli, Anil Veerendra Kotipalli, Seelam Nagendra
A hybrid quantum-chaotic encryption scheme for multi-scenario data security
Yuling Luo, Yunhua Ding, Xue Ouyang, Qiang Fu, Sheng Qin, Junxiu Liu, Yanyan Xu
A Hybrid Quantum-Classical Framework for Adaptive AI via Nonlinear Self-Reference
Göksel A.
A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Yixiong Chen
A Hybrid Quantum-Classical GAN with Classical Critic for Multi-Conditional Image Generation
Ta Z, Cai D.
A Hybrid Quantum-Classical Machine Learning Framework for Early and Accurate Diagnosis of Chronic Diseases
Khan A.
A hybrid quantum-classical neural network framework for genomic sequence classification
Riya Bansal, Nikhil Kumar Rajput, Megha Khanna
A Hybrid Quantum–Classical Machine Learning Framework for Black Carbon Forecasting
Isaac Oliva-González, Lucy T. González, Oscar D. Lara-Montaño, Aldo I. Ramirez, Alberto Mendoza, Ilse María Hernández-Romero
A Large-Scale Nanocrystal Database with Aligned Synthesis and Properties, Enabling Generative Inverse Design.
Gu K, Liang Y, Peng S, Guo A, Fu Y, Zhong H
A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization
Vojtěch Novák, Tomáš Bezděk, Ivan Zelinka, Swagatam Das, Martin Beseda
A low-circuit-depth quantum computing approach to the nuclear shell model.
Sarma C, Stevenson PD.
A Machine Learning-Assisted Highly Sensitive Photoelectrochemical Biosensor Based on Dual-Signal CuO Nanoparticles for the Combined Detection of Dual LncRNAs.
Mei M, Chen S, Li S, Liu S, Shang J, Ma Q, Wang K, Geng W, Yang R