Imagen de Portada

On the expressivity of embedding quantum kernels

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Gil-Fuster, Elies, Eisert, Jens, Dunjko, Vedran
Formato:	Preprint
Publicado:	2023
Materias:	Quantum Physics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

On the relation between trainability and dequantization of variational quantum learning models
por: Gil-Fuster, Elies, et al.
Publicado: (2024)

Double descent in quantum kernel methods
por: Kempkes, Marie, et al.
Publicado: (2025)

Optimal algorithmic complexity of inference in quantum kernel methods
por: Gil-Fuster, Elies, et al.
Publicado: (2026)

Understanding quantum machine learning also requires rethinking generalization
por: Gil-Fuster, Elies, et al.
Publicado: (2023)

Prospects for quantum advantage in machine learning from the representability of functions
por: Masot-Llima, Sergi, et al.
Publicado: (2025)

Opportunities and limitations of explaining quantum machine learning
por: Gil-Fuster, Elies, et al.
Publicado: (2024)

Exponential separations between classical and quantum learners
por: Gyurik, Casper, et al.
Publicado: (2023)

A PAC-Bayesian approach to generalization for quantum models
por: Rodriguez-Grasa, Pablo, et al.
Publicado: (2026)

Potential and limitations of random Fourier features for dequantizing quantum machine learning
por: Sweke, Ryan, et al.
Publicado: (2023)

Concept learning of parameterized quantum models from limited measurements
por: Gan, Beng Yee, et al.
Publicado: (2024)

Detecting underdetermination in parameterized quantum circuits
por: Kempkes, Marie, et al.
Publicado: (2025)

Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
por: Shen, Kevin, et al.
Publicado: (2026)

Shadows of quantum machine learning
por: Jerbi, Sofiene, et al.
Publicado: (2023)

Reinforcement Learning Assisted Recursive QAOA
por: Patel, Yash J., et al.
Publicado: (2022)

Curriculum reinforcement learning for quantum architecture search under hardware errors
por: Patel, Yash J., et al.
Publicado: (2024)

Quantum computing and persistence in topological data analysis
por: Gyurik, Casper, et al.
Publicado: (2024)

Online learning of quantum processes
por: Raza, Asad, et al.
Publicado: (2024)

Enhancing variational quantum state diagonalization using reinforcement learning techniques
por: Kundu, Akash, et al.
Publicado: (2023)

Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
por: Kurkin, Andrii, et al.
Publicado: (2025)

Exponential quantum advantages in learning quantum observables from classical data
por: Molteni, Riccardo, et al.
Publicado: (2024)

Stochastic noise can be helpful for variational quantum algorithms
por: Liu, Junyu, et al.
Publicado: (2022)

On the similarity of bandwidth-tuned quantum kernels and classical kernels
por: Flórez-Ablan, Roberto, et al.
Publicado: (2025)

Towards efficient quantum algorithms for diffusion probabilistic models
por: Wang, Yunfei, et al.
Publicado: (2025)

Kernel-based dequantization of variational QML without Random Fourier Features
por: Sweke, Ryan, et al.
Publicado: (2025)

Automatic and effective discovery of quantum kernels
por: Incudini, Massimiliano, et al.
Publicado: (2022)

Exponential concentration in quantum kernel methods
por: Thanasilp, Supanut, et al.
Publicado: (2022)

Towards provably efficient quantum algorithms for large-scale machine-learning models
por: Liu, Junyu, et al.
Publicado: (2023)

Neural auto-designer for enhanced quantum kernels
por: Lei, Cong, et al.
Publicado: (2024)

Hybrid model of the kernel method for quantum computers
por: de Borba, Jhordan Silveira, et al.
Publicado: (2024)

Satellite image classification with neural quantum kernels
por: Rodriguez-Grasa, Pablo, et al.
Publicado: (2024)

Shot-frugal and Robust quantum kernel classifiers
por: Shastry, Abhay, et al.
Publicado: (2022)

Improved separation between quantum and classical computers for sampling and functional tasks
por: Marshall, Simon C., et al.
Publicado: (2024)

On the average-case complexity of learning output distributions of quantum circuits
por: Nietner, Alexander, et al.
Publicado: (2023)

Multi-channel convolutional neural quantum embedding
por: Kim, Yujin, et al.
Publicado: (2025)

Multiple-basis representation of quantum states
por: Pérez-Salinas, Adrián, et al.
Publicado: (2024)

Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)
por: Bokov, Vasily, et al.
Publicado: (2026)

Artificially intelligent Maxwell's demon for optimal control of open quantum systems
por: Erdman, Paolo Andrea, et al.
Publicado: (2024)

Weighted Approximate Quantum Natural Gradient for Variational Quantum Eigensolver
por: Shi, Chenyu, et al.
Publicado: (2025)

A quantum annealing approach to graph node embedding
por: Djidjev, Hristo N.
Publicado: (2025)

Non-variational supervised quantum kernel methods: a review
por: Tanner, John, et al.
Publicado: (2026)