Cover Image

The complexity of quantum support vector machines

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gentinetta, Gian, Thomsen, Arne, Sutter, David, Woerner, Stefan
Format:	Preprint
Published:	2022
Subjects:	Quantum Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Approximate Quantum Fourier Transform in Logarithmic Depth on a Line
by: Bäumer, Elisa, et al.
Published: (2025)

Correcting and extending Trotterized quantum many-body dynamics
by: Gentinetta, Gian, et al.
Published: (2025)

Neural quantum support vector data description for one-class classification
by: Im, Changjae, et al.
Published: (2026)

Overhead-constrained circuit knitting for variational quantum dynamics
by: Gentinetta, Gian, et al.
Published: (2023)

Reinforcement learning-based architecture search for quantum machine learning
by: Rapp, Frederic, et al.
Published: (2024)

Fourier series weight in quantum machine learning
by: Atchade-Adelomou, Parfait, et al.
Published: (2023)

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

Bayesian quantum sensing using graybox machine learning
by: Youssry, Akram, et al.
Published: (2026)

Expressive equivalence of classical and quantum restricted Boltzmann machines
by: Demidik, Maria, et al.
Published: (2025)

Photovoltaic power forecasting using quantum machine learning
by: Sagingalieva, Asel, et al.
Published: (2023)

Can machine learning for quantum-gas experiments be explainable?
by: Zwolak, I. B. Spielman amd J. P.
Published: (2026)

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

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

Variational decision diagrams for quantum-inspired machine learning applications
by: Vargas-Calderón, Vladimir, et al.
Published: (2025)

Symmetry breaking in geometric quantum machine learning in the presence of noise
by: Tüysüz, Cenk, et al.
Published: (2024)

Superior resilience to poisoning and amenability to unlearning in quantum machine learning
by: Chen, Yu-Qin, et al.
Published: (2025)

A general learning scheme for classical and quantum Ising machines
by: Schmid, Ludwig, et al.
Published: (2023)

Spectral methods: crucial for machine learning, natural for quantum computers?
by: Belis, Vasilis, et al.
Published: (2026)

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

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

Information plane and compression-gnostic feedback in quantum machine learning
by: Haboury, Nathan, et al.
Published: (2024)

Structured quantum learning via em algorithm for Boltzmann machines
by: Kimura, Takeshi, et al.
Published: (2025)

Shadows of quantum machine learning
by: Jerbi, Sofiene, et al.
Published: (2023)

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

Dimension reduction with structure-aware quantum circuits for hybrid machine learning
by: Daskin, Ammar
Published: (2025)

The role of data-induced randomness in quantum machine learning classification tasks
by: Casas, Berta, et al.
Published: (2024)

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

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

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

Blind quantum machine learning with quantum bipartite correlator
by: Li, Changhao, et al.
Published: (2023)

Quantum Finite Temperature Lanczos Method
by: Gentinetta, Gian, et al.
Published: (2026)

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

Better than classical? The subtle art of benchmarking quantum machine learning models
by: Bowles, Joseph, et al.
Published: (2024)

Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
by: Candelori, Luca, et al.
Published: (2024)

Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
by: Jacob, Bruno, et al.
Published: (2025)

Training-efficient density quantum machine learning
by: Coyle, Brian, et al.
Published: (2024)

The interplay of robustness and generalization in quantum machine learning
by: Berberich, Julian, et al.
Published: (2025)

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

Symmetry-invariant quantum machine learning force fields
by: Le, Isabel Nha Minh, et al.
Published: (2023)

Generative modeling using evolved quantum Boltzmann machines
by: Wilde, Mark M.
Published: (2025)