A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead
K Zaman, A Marchisio, MA Hanif… - arXiv preprint arXiv …, 2023 - arxiv.org
Quantum Computing (QC) claims to improve the efficiency of solving complex problems,
compared to classical computing. When QC is applied to Machine Learning (ML) …
compared to classical computing. When QC is applied to Machine Learning (ML) …
Exponential concentration and untrainability in quantum kernel methods
Kernel methods in Quantum Machine Learning (QML) have recently gained significant
attention as a potential candidate for achieving a quantum advantage in data analysis …
attention as a potential candidate for achieving a quantum advantage in data analysis …
Trainability barriers and opportunities in quantum generative modeling
Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …
promise for achieving an advantage using quantum hardware. In this work, we investigate …
Generative quantum machine learning via denoising diffusion probabilistic models
Deep generative models are key-enabling technology to computer vision, text generation,
and large language models. Denoising diffusion probabilistic models (DDPMs) have …
and large language models. Denoising diffusion probabilistic models (DDPMs) have …
Trainability enhancement of parameterized quantum circuits via reduced-domain parameter initialization
Parameterized quantum circuits (PQCs) have been widely used as a machine learning
model to explore the potential of achieving quantum advantages for various tasks. However …
model to explore the potential of achieving quantum advantages for various tasks. However …
Statistical analysis of quantum state learning process in quantum neural networks
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term
quantum advantage in various fields, where many applications can be viewed as learning a …
quantum advantage in various fields, where many applications can be viewed as learning a …
Quantum self-attention neural networks for text classification
An emerging direction of quantum computing is to establish meaningful quantum
applications in various fields of artificial intelligence, including natural language processing …
applications in various fields of artificial intelligence, including natural language processing …
Expressibility-induced concentration of quantum neural tangent kernels
Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit, which is of …
performance of quantum machine learning models in the infinite-width limit, which is of …
CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine
learning pipeline specifically developed to address the computational challenges …
learning pipeline specifically developed to address the computational challenges …
Quantum mixed-state self-attention network
F Chen, Q Zhao, L Feng, C Chen, Y Lin, J Lin - Neural Networks, 2025 - Elsevier
Attention mechanisms have revolutionized natural language processing. Combining them
with quantum computing aims to further advance this technology. This paper introduces a …
with quantum computing aims to further advance this technology. This paper introduces a …