Optimal algorithmic complexity of inference in quantum kernel methods
Fuente:
arXiv
Saved in:
| Main Authors: | Gil-Fuster, Elies, Shin, Seongwook, Jerbi, Sofiene, Eisert, Jens, Kramer, Maximilian J. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the expressivity of embedding quantum kernels
by: Gil-Fuster, Elies, et al.
Published: (2023)
by: Gil-Fuster, Elies, et al.
Published: (2023)
Potential and limitations of random Fourier features for dequantizing quantum machine learning
by: Sweke, Ryan, et al.
Published: (2023)
by: Sweke, Ryan, et al.
Published: (2023)
Understanding quantum machine learning also requires rethinking generalization
by: Gil-Fuster, Elies, et al.
Published: (2023)
by: Gil-Fuster, Elies, et al.
Published: (2023)
Prospects for quantum advantage in machine learning from the representability of functions
by: Masot-Llima, Sergi, et al.
Published: (2025)
by: Masot-Llima, Sergi, et al.
Published: (2025)
Double descent in quantum kernel methods
by: Kempkes, Marie, et al.
Published: (2025)
by: Kempkes, Marie, et al.
Published: (2025)
Opportunities and limitations of explaining quantum machine learning
by: Gil-Fuster, Elies, et al.
Published: (2024)
by: Gil-Fuster, Elies, et al.
Published: (2024)
Kernel-based dequantization of variational QML without Random Fourier Features
by: Sweke, Ryan, et al.
Published: (2025)
by: Sweke, Ryan, et al.
Published: (2025)
A PAC-Bayesian approach to generalization for quantum models
by: Rodriguez-Grasa, Pablo, et al.
Published: (2026)
by: Rodriguez-Grasa, Pablo, et al.
Published: (2026)
A quantum-classical reinforcement learning model to play Atari games
by: Freinberger, Dominik, et al.
Published: (2024)
by: Freinberger, Dominik, et al.
Published: (2024)
On the relation between trainability and dequantization of variational quantum learning models
by: Gil-Fuster, Elies, et al.
Published: (2024)
by: Gil-Fuster, Elies, et al.
Published: (2024)
Entanglement theory with limited computational resources
by: Leone, Lorenzo, et al.
Published: (2025)
by: Leone, Lorenzo, et al.
Published: (2025)
Concept learning of parameterized quantum models from limited measurements
by: Gan, Beng Yee, et al.
Published: (2024)
by: Gan, Beng Yee, et al.
Published: (2024)
Shadows of quantum machine learning
by: Jerbi, Sofiene, et al.
Published: (2023)
by: Jerbi, Sofiene, et al.
Published: (2023)
Reinforcement Learning Assisted Recursive QAOA
by: Patel, Yash J., et al.
Published: (2022)
by: Patel, Yash J., et al.
Published: (2022)
Variational measurement-based quantum computation for generative modeling
by: Majumder, Arunava, et al.
Published: (2023)
by: Majumder, Arunava, et al.
Published: (2023)
Stochastic noise can be helpful for variational quantum algorithms
by: Liu, Junyu, et al.
Published: (2022)
by: Liu, Junyu, et al.
Published: (2022)
The power and limitations of learning quantum dynamics incoherently
by: Jerbi, Sofiene, et al.
Published: (2023)
by: Jerbi, Sofiene, et al.
Published: (2023)
New perspectives on quantum kernels through the lens of entangled tensor kernels
by: Shin, Seongwook, et al.
Published: (2025)
by: Shin, Seongwook, et al.
Published: (2025)
Towards efficient quantum algorithms for diffusion probabilistic models
by: Wang, Yunfei, et al.
Published: (2025)
by: Wang, Yunfei, et al.
Published: (2025)
Efficient distributed inner product estimation via Pauli sampling
by: Hinsche, Marcel, et al.
Published: (2024)
by: Hinsche, Marcel, et al.
Published: (2024)
The computational two-way quantum capacity
by: Meyer, Johannes Jakob, et al.
Published: (2026)
by: Meyer, Johannes Jakob, et al.
Published: (2026)
Towards provably efficient quantum algorithms for large-scale machine-learning models
by: Liu, Junyu, et al.
Published: (2023)
by: Liu, Junyu, et al.
Published: (2023)
Online learning of quantum processes
by: Raza, Asad, et al.
Published: (2024)
by: Raza, Asad, et al.
Published: (2024)
Exponential concentration in quantum kernel methods
by: Thanasilp, Supanut, et al.
Published: (2022)
by: Thanasilp, Supanut, et al.
Published: (2022)
Tight inapproximability of max-LINSAT and implications for decoded quantum interferometry
by: Kramer, Maximilian J., et al.
Published: (2026)
by: Kramer, Maximilian J., et al.
Published: (2026)
On the average-case complexity of learning output distributions of quantum circuits
by: Nietner, Alexander, et al.
Published: (2023)
by: Nietner, Alexander, et al.
Published: (2023)
Hybrid model of the kernel method for quantum computers
by: de Borba, Jhordan Silveira, et al.
Published: (2024)
by: de Borba, Jhordan Silveira, et al.
Published: (2024)
A measurement-driven quantum algorithm for SAT: Performance guarantees via spectral gaps and measurement parallelization
by: Schreiber, Franz J., et al.
Published: (2025)
by: Schreiber, Franz J., et al.
Published: (2025)
Non-variational supervised quantum kernel methods: a review
by: Tanner, John, et al.
Published: (2026)
by: Tanner, John, 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)
by: Vasques, Xavier, et al.
Published: (2025)
Maritime object classification with SAR imagery using quantum kernel methods
by: Tanner, John, et al.
Published: (2025)
by: Tanner, John, et al.
Published: (2025)
On the similarity of bandwidth-tuned quantum kernels and classical kernels
by: Flórez-Ablan, Roberto, et al.
Published: (2025)
by: Flórez-Ablan, Roberto, et al.
Published: (2025)
Computational relative entropy
by: Meyer, Johannes Jakob, et al.
Published: (2025)
by: Meyer, Johannes Jakob, et al.
Published: (2025)
An unconditional distribution learning advantage with shallow quantum circuits
by: Pirnay, N., et al.
Published: (2024)
by: Pirnay, N., et al.
Published: (2024)
Automatic and effective discovery of quantum kernels
by: Incudini, Massimiliano, et al.
Published: (2022)
by: Incudini, Massimiliano, et al.
Published: (2022)
Neural auto-designer for enhanced quantum kernels
by: Lei, Cong, et al.
Published: (2024)
by: Lei, Cong, et al.
Published: (2024)
Satellite image classification with neural quantum kernels
by: Rodriguez-Grasa, Pablo, et al.
Published: (2024)
by: Rodriguez-Grasa, Pablo, et al.
Published: (2024)
Shot-frugal and Robust quantum kernel classifiers
by: Shastry, Abhay, et al.
Published: (2022)
by: Shastry, Abhay, et al.
Published: (2022)
Semi-device-independently characterizing quantum temporal correlations
by: Chen, Shin-Liang, et al.
Published: (2023)
by: Chen, Shin-Liang, et al.
Published: (2023)
Artificially intelligent Maxwell's demon for optimal control of open quantum systems
by: Erdman, Paolo Andrea, et al.
Published: (2024)
by: Erdman, Paolo Andrea, et al.
Published: (2024)
Similar Items
-
On the expressivity of embedding quantum kernels
by: Gil-Fuster, Elies, et al.
Published: (2023) -
Potential and limitations of random Fourier features for dequantizing quantum machine learning
by: Sweke, Ryan, et al.
Published: (2023) -
Understanding quantum machine learning also requires rethinking generalization
by: Gil-Fuster, Elies, et al.
Published: (2023) -
Prospects for quantum advantage in machine learning from the representability of functions
by: Masot-Llima, Sergi, et al.
Published: (2025) -
Double descent in quantum kernel methods
by: Kempkes, Marie, et al.
Published: (2025)