Imagen de Portada

Exponential concentration in quantum kernel methods

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Thanasilp, Supanut, Wang, Samson, Cerezo, M., Holmes, Zoë
Formato:	Preprint
Publicado:	2022
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

Variational quantum simulation: a case study for understanding warm starts
por: Puig, Ricard, et al.
Publicado: (2024)

Efficient quantum-enhanced classical simulation for patches of quantum landscapes
por: Lerch, Sacha, et al.
Publicado: (2024)

A unifying account of warm start guarantees for patches of quantum landscapes
por: Mhiri, Hela, et al.
Publicado: (2025)

IQP Born Machines under Data-dependent and Agnostic Initialization Strategies
por: Lerch, Sacha, et al.
Publicado: (2026)

Barren Plateaus in Variational Quantum Computing
por: Larocca, Martin, et al.
Publicado: (2024)

On fundamental aspects of quantum extreme learning machines
por: Xiong, Weijie, et al.
Publicado: (2023)

Pitfalls when tackling the exponential concentration of parameterized quantum models
por: Saem, Reyhaneh Aghaei, et al.
Publicado: (2025)

Role of scrambling and noise in temporal information processing with quantum systems
por: Xiong, Weijie, et al.
Publicado: (2025)

Trainability barriers and opportunities in quantum generative modeling
por: Rudolph, Manuel S., et al.
Publicado: (2023)

Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
por: Srimahajariyapong, Kasidit, et al.
Publicado: (2025)

Does provable absence of barren plateaus imply classical simulability?
por: Cerezo, M., et al.
Publicado: (2023)

Quantum Convolutional Neural Networks are Effectively Classically Simulable
por: Bermejo, Pablo, et al.
Publicado: (2024)

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

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

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

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

Maritime object classification with SAR imagery using quantum kernel methods
por: Tanner, John, et al.
Publicado: (2025)

Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
por: Wang, Samson, et al.
Publicado: (2021)

Architectures and random properties of symplectic quantum circuits
por: García-Martín, Diego, et al.
Publicado: (2024)

Effects of noise on the overparametrization of quantum neural networks
por: García-Martín, Diego, et al.
Publicado: (2023)

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

Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
por: Agliardi, Gabriele, et al.
Publicado: (2024)

Noise-Induced Barren Plateaus in Variational Quantum Algorithms
por: Wang, Samson, et al.
Publicado: (2020)

On the expressivity of embedding quantum kernels
por: Gil-Fuster, Elies, et al.
Publicado: (2023)

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

A semi-agnostic ansatz with variable structure for quantum machine learning
por: Bilkis, M., et al.
Publicado: (2021)

Provable and scalable quantum Gaussian processes for quantum learning
por: Jäger, Jonas, et al.
Publicado: (2026)

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

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

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

Learning out-of-time-ordered correlators with classical kernel methods
por: Tanner, John, et al.
Publicado: (2024)

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

Dynamical simulation via quantum machine learning with provable generalization
por: Gibbs, Joe, et al.
Publicado: (2022)

Benchmarking quantum machine learning kernel training for classification tasks
por: Alvarez-Estevez, Diego
Publicado: (2024)

On the universality of $S_n$-equivariant $k$-body gates
por: Kazi, Sujay, et al.
Publicado: (2023)

Moments of quantum channel ensembles
por: Duschenes, Matthew, et al.
Publicado: (2025)

Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States
por: Puig, Ricard, et al.
Publicado: (2026)

Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
por: Vasques, Xavier, et al.
Publicado: (2025)

The power and limitations of learning quantum dynamics incoherently
por: Jerbi, Sofiene, et al.
Publicado: (2023)

Quantum neural networks form Gaussian processes
por: García-Martín, Diego, et al.
Publicado: (2023)