Cover Image

Kernel-based dequantization of variational QML without Random Fourier Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sweke, Ryan, Shin, Seongwook, Gil-Fuster, Elies
Format:	Preprint
Published:	2025
Subjects:	Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

New perspectives on quantum kernels through the lens of entangled tensor kernels
by: Shin, Seongwook, et al.
Published: (2025)

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

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

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

QML Essentials -- A framework for working with Quantum Fourier Models
by: Strobl, Melvin, et al.
Published: (2025)

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

Distributed Quantum Property Testing with Communication Constraints
by: Doosti, Mina, et al.
Published: (2026)

Dequantizing quantum machine learning models using tensor networks
by: Shin, Seongwook, et al.
Published: (2023)

Efficient Quantum Gibbs Sampling with Local Circuits
by: Hahn, Dominik, et al.
Published: (2025)

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

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

Domain-Aware Quantum Circuit for QML
by: Singh, Gurinder, et al.
Published: (2025)

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

Optimizing Quantum Embedding using Genetic Algorithm for QML Applications
by: Phalak, Koustubh, et al.
Published: (2024)

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

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
by: Layden, David, et al.
Published: (2025)

Dynamic parameterized quantum circuits: expressive and barren-plateau free
by: Deshpande, Abhinav, et al.
Published: (2024)

AI-driven Reverse Engineering of QML Models
by: Ghosh, Archisman, et al.
Published: (2024)

Long Range Frequency Tuning for QML
by: Poppel, Michael, et al.
Published: (2026)

On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features
by: Sahebi, Mehrad, et al.
Published: (2025)

Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements
by: Tomar, Sahil, et al.
Published: (2025)

AutoQML: A Framework for Automated Quantum Machine Learning
by: Roth, Marco, et al.
Published: (2025)

Learning topological states from randomized measurements using variational tensor network tomography
by: Teng, Yanting, et al.
Published: (2024)

Random Circuit Sampling: Fourier Expansion and Statistics
by: Kalai, Gil, et al.
Published: (2024)

Characterizing Scaling Trends of Post-Compilation Circuit Resources for NISQ-era QML Models
by: Bhattacharjee, Rupayan, et al.
Published: (2025)

Classical Verification of Quantum Learning
by: Caro, Matthias C., et al.
Published: (2023)

Quantization, dequantization, and distinguished states
by: Hawkins, Eli, et al.
Published: (2022)

On Certified Randomness from Fourier Sampling or Random Circuit Sampling
by: Bassirian, Roozbeh, et al.
Published: (2021)

CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting
by: Periyasamy, Maniraman, et al.
Published: (2025)

Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
by: Ahmad, Syed Farhan, et al.
Published: (2026)

Optimizing Quantum Fourier Transformation (QFT) Kernels for Modern NISQ and FT Architectures
by: Jin, Yuwei, et al.
Published: (2024)

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

QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
by: Yeniaras, Esra
Published: (2026)

Time-dependent variational Monte Carlo without bias
by: Krinitsin, Wladislaw, et al.
Published: (2026)

Interactive proofs for verifying (quantum) learning and testing
by: Caro, Matthias C., et al.
Published: (2024)

Hamiltonian variational ansatz without barren plateaus
by: Park, Chae-Yeun, et al.
Published: (2023)

Classical Simulation of Noiseless Quantum Dynamics without Randomness
by: Xu, Jue, et al.
Published: (2026)

The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs
by: Salmenperä, Ilmo, et al.
Published: (2024)