Saved in:
| Main Author: | Ohno, Hiroshi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15693 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Generalization analysis of quantum neural networks using dynamical Lie algebras
by: Ohno, Hiroshi
Published: (2025)
by: Ohno, Hiroshi
Published: (2025)
Lie-Algebraic Analysis of Generators: Approximation-Error Bounds and Barren-Plateau Heuristics
by: Ohno, Hiroshi
Published: (2026)
by: Ohno, Hiroshi
Published: (2026)
Provably Trainable Rotationally Equivariant Quantum Machine Learning
by: West, Maxwell T., et al.
Published: (2023)
by: West, Maxwell T., et al.
Published: (2023)
Approximate Cosine Similarity Estimation via an Angle-Encoding Hadamard Test
by: Ohno, Hiroshi
Published: (2026)
by: Ohno, Hiroshi
Published: (2026)
Comment on "Provably Trainable Rotationally Equivariant Quantum Machine Learning"
by: Xiao, Zhiming, et al.
Published: (2025)
by: Xiao, Zhiming, et al.
Published: (2025)
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
by: Anschuetz, Eric R., et al.
Published: (2024)
by: Anschuetz, Eric R., et al.
Published: (2024)
Improving Generalization and Trainability of Quantum Eigensolvers via Graph Neural Encoding
by: Lee, Jungyun, et al.
Published: (2026)
by: Lee, Jungyun, et al.
Published: (2026)
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
by: Monbroussou, Léo, et al.
Published: (2023)
by: Monbroussou, Léo, et al.
Published: (2023)
On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
by: Tao, Han-Xiao, et al.
Published: (2025)
by: Tao, Han-Xiao, et al.
Published: (2025)
Quantum Classifiers with Trainable Kernel
by: Xu, Li, et al.
Published: (2025)
by: Xu, Li, et al.
Published: (2025)
Optimal Quantum Purity Amplification
by: Li, Zhaoyi, et al.
Published: (2024)
by: Li, Zhaoyi, et al.
Published: (2024)
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
by: Shen, Kevin, et al.
Published: (2026)
by: Shen, Kevin, et al.
Published: (2026)
Local-Observable-Guided Generative Quantum Circuits for Degenerate Ground Spaces
by: Chen, Yiying, et al.
Published: (2026)
by: Chen, Yiying, et al.
Published: (2026)
Local Purity Distillation in Quantum Systems: Exploring the Complementarity Between Purity and Entanglement
by: Ganardi, Ray, et al.
Published: (2023)
by: Ganardi, Ray, et al.
Published: (2023)
Observability Architecture for Quantum-Centric Supercomputing Workflows
by: Kanazawa, Naoki, et al.
Published: (2025)
by: Kanazawa, Naoki, et al.
Published: (2025)
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
by: Wang, Samson, et al.
Published: (2021)
by: Wang, Samson, et al.
Published: (2021)
Protocols for Trainable and Differentiable Quantum Generative Modelling
by: Kyriienko, Oleksandr, et al.
Published: (2022)
by: Kyriienko, Oleksandr, et al.
Published: (2022)
Empirical Study of Observable Sets in Multiclass Quantum Classification
by: Sebastian, Paul San, et al.
Published: (2026)
by: Sebastian, Paul San, et al.
Published: (2026)
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models
by: Kurkin, Andrii, et al.
Published: (2026)
by: Kurkin, Andrii, et al.
Published: (2026)
Learning Robust Observable to Address Noise in Quantum Machine Learning
by: Khanal, Bikram, et al.
Published: (2024)
by: Khanal, Bikram, et al.
Published: (2024)
Quantum Engineering of Qudits with Interpretable Machine Learning
by: Mayevsky, Yule, et al.
Published: (2025)
by: Mayevsky, Yule, et al.
Published: (2025)
Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
by: Faria, Arthur M., et al.
Published: (2025)
by: Faria, Arthur M., et al.
Published: (2025)
Quantum Coordination without Conditioning under Restricted Information
by: Khan, Faisal Shah
Published: (2026)
by: Khan, Faisal Shah
Published: (2026)
Quantum Purity Amplification for Arbitrary Eigenstates and Multiple Outputs
by: Li, Zhaoyi, et al.
Published: (2026)
by: Li, Zhaoyi, et al.
Published: (2026)
Trainable Quantum Spectral Models for Partial Differential Equations
by: Mejia, Gabriel, et al.
Published: (2026)
by: Mejia, Gabriel, et al.
Published: (2026)
Diagnosing Quantum Circuits: Noise Robustness, Trainability, and Expressibility
by: Shao, Yuguo, et al.
Published: (2025)
by: Shao, Yuguo, et al.
Published: (2025)
Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement
by: Khan, Saeed A., et al.
Published: (2023)
by: Khan, Saeed A., et al.
Published: (2023)
Enhancing Circuit Trainability with Selective Gate Activation Strategy
by: Cho, Jeihee, et al.
Published: (2025)
by: Cho, Jeihee, et al.
Published: (2025)
Trainability Beyond Linearity in Variational Quantum Objectives
by: Ma, Gordon, et al.
Published: (2026)
by: Ma, Gordon, et al.
Published: (2026)
Mitigating Precision Errors in Quantum Annealing via Coefficient Reduction of Embedded Hamiltonians
by: Ohno, Kentaro, et al.
Published: (2026)
by: Ohno, Kentaro, et al.
Published: (2026)
NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks
by: Kashif, Muhammad, et al.
Published: (2025)
by: Kashif, Muhammad, et al.
Published: (2025)
Quantum Wall States for Noise Mitigation and Eternal Purity Bounds
by: Casanova, Miguel, et al.
Published: (2025)
by: Casanova, Miguel, et al.
Published: (2025)
Purity-Assisted Zero-Noise Extrapolation for Quantum Error Mitigation
by: Jin, Tian-Ren, et al.
Published: (2023)
by: Jin, Tian-Ren, et al.
Published: (2023)
The Many Inconsistencies of the Purity-Mixture Distinction in Standard Quantum Mechanics
by: de Ronde, Christian, et al.
Published: (2022)
by: de Ronde, Christian, et al.
Published: (2022)
DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint
by: Zhang, Chenyi, et al.
Published: (2025)
by: Zhang, Chenyi, et al.
Published: (2025)
How to Partition a Quantum Observable
by: Webb, Caleb M., et al.
Published: (2024)
by: Webb, Caleb M., et al.
Published: (2024)
Special-Unitary Parameterization for Trainable Variational Quantum Circuits
by: Chen, Kuan-Cheng, et al.
Published: (2025)
by: Chen, Kuan-Cheng, et al.
Published: (2025)
Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies
by: Zhuang, Jun, et al.
Published: (2024)
by: Zhuang, Jun, et al.
Published: (2024)
Rethinking Expressibility-Trainability Trade-off in Hybrid Quantum Neural Networks
by: Kashif, Muhammad, et al.
Published: (2026)
by: Kashif, Muhammad, et al.
Published: (2026)
Machine Failure Detection Based on Projected Quantum Models
by: Bowden, Larry, et al.
Published: (2026)
by: Bowden, Larry, et al.
Published: (2026)
Similar Items
-
Generalization analysis of quantum neural networks using dynamical Lie algebras
by: Ohno, Hiroshi
Published: (2025) -
Lie-Algebraic Analysis of Generators: Approximation-Error Bounds and Barren-Plateau Heuristics
by: Ohno, Hiroshi
Published: (2026) -
Provably Trainable Rotationally Equivariant Quantum Machine Learning
by: West, Maxwell T., et al.
Published: (2023) -
Approximate Cosine Similarity Estimation via an Angle-Encoding Hadamard Test
by: Ohno, Hiroshi
Published: (2026) -
Comment on "Provably Trainable Rotationally Equivariant Quantum Machine Learning"
by: Xiao, Zhiming, et al.
Published: (2025)