Imagen de Portada

Comment on "Provably Trainable Rotationally Equivariant Quantum Machine Learning"

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiao, Zhiming, Li, Ting
Formato:	Preprint
Publicado:	2025
Materias:	Quantum Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Provably Trainable Rotationally Equivariant Quantum Machine Learning
por: West, Maxwell T., et al.
Publicado: (2023)

Quantum Classifiers with Trainable Kernel
por: Xu, Li, et al.
Publicado: (2025)

On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
por: Tao, Han-Xiao, et al.
Publicado: (2025)

Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
por: Anschuetz, Eric R., et al.
Publicado: (2024)

Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
por: Monbroussou, Léo, et al.
Publicado: (2023)

Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
por: Shen, Kevin, et al.
Publicado: (2026)

Trainability Beyond Linearity in Variational Quantum Objectives
por: Ma, Gordon, et al.
Publicado: (2026)

Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
por: Hamid, Eyad I. B.
Publicado: (2026)

Diagnosing Quantum Circuits: Noise Robustness, Trainability, and Expressibility
por: Shao, Yuguo, et al.
Publicado: (2025)

Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement
por: Khan, Saeed A., et al.
Publicado: (2023)

Trainable Quantum Spectral Models for Partial Differential Equations
por: Mejia, Gabriel, et al.
Publicado: (2026)

Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
por: Faria, Arthur M., et al.
Publicado: (2025)

Observable-Guided Generator Selection for Improving Trainability in Quantum Machine Learning with a $ \mathfrak{g} $-Purity Interpretation under Restricted Settings
por: Ohno, Hiroshi
Publicado: (2026)

On the Trainability and Classical Simulability of Learning Matrix Product States Variationally
por: Basheer, Afrad, et al.
Publicado: (2024)

Learning Equivariant Maps with Variational Quantum Circuits
por: Bradshaw, Zachary P., et al.
Publicado: (2024)

Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)
por: Bokov, Vasily, et al.
Publicado: (2026)

DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint
por: Zhang, Chenyi, et al.
Publicado: (2025)

Improving Generalization and Trainability of Quantum Eigensolvers via Graph Neural Encoding
por: Lee, Jungyun, et al.
Publicado: (2026)

Rethinking Expressibility-Trainability Trade-off in Hybrid Quantum Neural Networks
por: Kashif, Muhammad, et al.
Publicado: (2026)

Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models
por: Kurkin, Andrii, et al.
Publicado: (2026)

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
por: Chen, Kuan-Cheng, et al.
Publicado: (2025)

Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies
por: Zhuang, Jun, et al.
Publicado: (2024)

Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
por: Tran, Ban Q., et al.
Publicado: (2026)

Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization
por: Wang, Yabo, et al.
Publicado: (2023)

Protocols for Trainable and Differentiable Quantum Generative Modelling
por: Kyriienko, Oleksandr, et al.
Publicado: (2022)

From Provable to Practical: A Problem-Driven Survey of Classical and Machine-Learning Defenses for DV/CV Quantum Key Distribution
por: Al-Mohammed, Hasan Abbas, et al.
Publicado: (2026)

Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
por: Chandrasekhar, Swathi, et al.
Publicado: (2025)

Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
por: Meng, Qingyu, et al.
Publicado: (2026)

Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
por: Sadhu, Abhishek, et al.
Publicado: (2026)

Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics
por: Peng, Changnan, et al.
Publicado: (2024)

Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms
por: Kashif, Muhammad, et al.
Publicado: (2026)

Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits
por: Kashif, Muhammad, et al.
Publicado: (2026)

Trainability of Parametrised Linear Combinations of Unitaries
por: Khatri, Nikhil, et al.
Publicado: (2025)

Provable and Verifiable Quantum Advantage in Sample Complexity
por: Benedetti, Marcello, et al.
Publicado: (2025)

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

Equivariant Machine Learning Decoder for 3D Toric Codes
por: Weissl, Oliver, et al.
Publicado: (2024)

Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis
por: Yeung, Richie, et al.
Publicado: (2026)

Efficient Learning of Long-Range and Equivariant Quantum Systems
por: Šmíd, Štěpán, et al.
Publicado: (2023)

A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems
por: Tran, Ban Q., et al.
Publicado: (2026)

A Digital Spreading Framework for Quantum Expectation Computation Without Rotation Gates or Arithmetic Circuits
por: Kao, Yu-Ting, et al.
Publicado: (2026)