Cover Image

Non-variational supervised quantum kernel methods: a review

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tanner, John, Kam, Chon-Fai, Wang, Jingbo
Format:	Preprint
Published:	2026
Subjects:	Quantum Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Nonlinear optical analogues of quantum phase transitions in a squeezing-enhanced LMG model
by: Kam, Chon-Fai
Published: (2025)

Nonlinear optical realization of non-integrable phases accompanying quantum phase transitions
by: Kam, Chon-Fai
Published: (2025)

Majorana Constellations: A Geometric Lens on Multipartite Entanglement and Geometric Phases
by: Kam, Chon-Fai
Published: (2026)

Three-Axis Spin Squeezed States Associated with Excited-State Quantum Phase Transitions
by: Kam, Chon-Fai
Published: (2025)

Analytical approximations for generalized quantum Rabi models
by: Kam, Chon-Fai, et al.
Published: (2024)

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

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

Sub-microsecond high-fidelity dispersive readout of a spin qubit with squeezed photons
by: Kam, Chon-Fai, et al.
Published: (2023)

Fast and high-fidelity dispersive readout of a spin qubit via squeezing and resonator nonlinearity
by: Kam, Chon-Fai, et al.
Published: (2024)

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

Super-Poissonian Squeezed Light in the Deep Strong Regime of the Quantum Rabi Model
by: Kam, Chon-Fai, et al.
Published: (2024)

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

Beyond Boson Sampling: Higher Spin Sampling as a Practical Path to Quantum Supremacy
by: Kam, Chon-Fai, et al.
Published: (2025)

Quantum-Secured Device-Independent Global Positioning System
by: Kam, Chon-Fai, et al.
Published: (2025)

Quantum Supremacy through Fock State $q$ boson Sampling with Transmon Qubits
by: Kam, Chon-Fai, et al.
Published: (2025)

What is Entanglement?
by: Kam, Chon-Fai, et al.
Published: (2024)

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

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

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

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

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

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

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

Verifiable cloud-based variational quantum algorithms
by: Yang, Junhong, et al.
Published: (2024)

Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
by: Kam, Chon-Fai, et al.
Published: (2026)

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

Post-variational quantum neural networks
by: Huang, Po-Wei, et al.
Published: (2023)

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

Learning quantum symmetries with interactive quantum-classical variational algorithms
by: Lu, Jonathan Z., et al.
Published: (2022)

On the explainability of quantum neural networks based on variational quantum circuits
by: Daskin, Ammar
Published: (2023)

Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
by: Bhowmick, Rahul, et al.
Published: (2025)

Clustering by Contour coreset and variational quantum eigensolver
by: Yung, Canaan, et al.
Published: (2023)

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

Fusion of classical and quantum kernels enables accurate and robust two-sample tests
by: Terada, Yu, et al.
Published: (2025)

Energy-dependent barren plateau in bosonic variational quantum circuits
by: Zhang, Bingzhi, et al.
Published: (2023)

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

Generative flow-based warm start of the variational quantum eigensolver
by: Zou, Hang, et al.
Published: (2025)

Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
by: Kundu, Akash
Published: (2024)