Towards efficient quantum algorithms for diffusion probabilistic models
Fuente:
arXiv
Guardado en:
| Autores principales: | Wang, Yunfei, Jiang, Ruoxi, Fan, Yingda, Jia, Xiaowei, Eisert, Jens, Liu, Junyu, Liu, Jin-Peng |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Towards provably efficient quantum algorithms for large-scale machine-learning models
por: Liu, Junyu, et al.
Publicado: (2023)
por: Liu, Junyu, et al.
Publicado: (2023)
Stochastic noise can be helpful for variational quantum algorithms
por: Liu, Junyu, et al.
Publicado: (2022)
por: Liu, Junyu, et al.
Publicado: (2022)
Optimal algorithmic complexity of inference in quantum kernel methods
por: Gil-Fuster, Elies, et al.
Publicado: (2026)
por: Gil-Fuster, Elies, et al.
Publicado: (2026)
Generative quantum machine learning via denoising diffusion probabilistic models
por: Zhang, Bingzhi, et al.
Publicado: (2023)
por: Zhang, Bingzhi, et al.
Publicado: (2023)
A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
por: Wang, Yunfei, et al.
Publicado: (2024)
por: Wang, Yunfei, et al.
Publicado: (2024)
Fundamental causal bounds of quantum random access memories
por: Wang, Yunfei, et al.
Publicado: (2023)
por: Wang, Yunfei, et al.
Publicado: (2023)
On the expressivity of embedding quantum kernels
por: Gil-Fuster, Elies, et al.
Publicado: (2023)
por: Gil-Fuster, Elies, et al.
Publicado: (2023)
Verifiable cloud-based variational quantum algorithms
por: Yang, Junhong, et al.
Publicado: (2024)
por: Yang, Junhong, et al.
Publicado: (2024)
Understanding quantum machine learning also requires rethinking generalization
por: Gil-Fuster, Elies, et al.
Publicado: (2023)
por: Gil-Fuster, Elies, et al.
Publicado: (2023)
Online learning of quantum processes
por: Raza, Asad, et al.
Publicado: (2024)
por: Raza, Asad, et al.
Publicado: (2024)
A PAC-Bayesian approach to generalization for quantum models
por: Rodriguez-Grasa, Pablo, et al.
Publicado: (2026)
por: Rodriguez-Grasa, Pablo, et al.
Publicado: (2026)
The curse of random quantum data
por: Zhang, Kaining, et al.
Publicado: (2024)
por: Zhang, Kaining, et al.
Publicado: (2024)
Prospects for quantum advantage in machine learning from the representability of functions
por: Masot-Llima, Sergi, et al.
Publicado: (2025)
por: Masot-Llima, Sergi, et al.
Publicado: (2025)
Opportunities and limitations of explaining quantum machine learning
por: Gil-Fuster, Elies, et al.
Publicado: (2024)
por: Gil-Fuster, Elies, et al.
Publicado: (2024)
Quantum-data-driven dynamical transition in quantum learning
por: Zhang, Bingzhi, et al.
Publicado: (2024)
por: Zhang, Bingzhi, et al.
Publicado: (2024)
Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy
por: Tu, Yue, et al.
Publicado: (2025)
por: Tu, Yue, et al.
Publicado: (2025)
Potential and limitations of random Fourier features for dequantizing quantum machine learning
por: Sweke, Ryan, et al.
Publicado: (2023)
por: Sweke, Ryan, et al.
Publicado: (2023)
Is data-efficient learning feasible with quantum models?
por: Sakhnenko, Alona, et al.
Publicado: (2025)
por: Sakhnenko, Alona, et al.
Publicado: (2025)
Estimating truncation effects of quantum bosonic systems using sampling algorithms
por: Hanada, Masanori, et al.
Publicado: (2022)
por: Hanada, Masanori, et al.
Publicado: (2022)
Dynamical transition in controllable quantum neural networks with large depth
por: Zhang, Bingzhi, et al.
Publicado: (2023)
por: Zhang, Bingzhi, et al.
Publicado: (2023)
On the average-case complexity of learning output distributions of quantum circuits
por: Nietner, Alexander, et al.
Publicado: (2023)
por: Nietner, Alexander, et al.
Publicado: (2023)
Learning with errors may remain hard against quantum holographic attacks
por: Wang, Yunfei, et al.
Publicado: (2025)
por: Wang, Yunfei, et al.
Publicado: (2025)
Artificially intelligent Maxwell's demon for optimal control of open quantum systems
por: Erdman, Paolo Andrea, et al.
Publicado: (2024)
por: Erdman, Paolo Andrea, et al.
Publicado: (2024)
Laziness, Barren Plateau, and Noise in Machine Learning
por: Liu, Junyu, et al.
Publicado: (2022)
por: Liu, Junyu, et al.
Publicado: (2022)
Quantum Data Center: Perspectives
por: Liu, Junyu, et al.
Publicado: (2023)
por: Liu, Junyu, et al.
Publicado: (2023)
On the physics of nested Markov models: a generalized probabilistic theory perspective
por: Zhang, Xingjian, et al.
Publicado: (2024)
por: Zhang, Xingjian, et al.
Publicado: (2024)
Learning quantum symmetries with interactive quantum-classical variational algorithms
por: Lu, Jonathan Z., et al.
Publicado: (2022)
por: Lu, Jonathan Z., et al.
Publicado: (2022)
Explainable quantum regression algorithm with encoded data structure
por: Wang, C. -C. Joseph, et al.
Publicado: (2023)
por: Wang, C. -C. Joseph, et al.
Publicado: (2023)
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
por: de Schoulepnikoff, Paulin, et al.
Publicado: (2025)
por: de Schoulepnikoff, Paulin, et al.
Publicado: (2025)
The Efficiency Frontier: Classical Shadows versus Quantum Footage
por: Ma, Shuowei, et al.
Publicado: (2025)
por: Ma, Shuowei, et al.
Publicado: (2025)
Interactive proofs for verifying (quantum) learning and testing
por: Caro, Matthias C., et al.
Publicado: (2024)
por: Caro, Matthias C., et al.
Publicado: (2024)
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
por: Bhowmick, Rahul, et al.
Publicado: (2025)
por: Bhowmick, Rahul, et al.
Publicado: (2025)
Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
por: Fürrutter, Florian, et al.
Publicado: (2025)
por: Fürrutter, Florian, et al.
Publicado: (2025)
Deep Stochastic Mechanics
por: Orlova, Elena, et al.
Publicado: (2023)
por: Orlova, Elena, et al.
Publicado: (2023)
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU($d$) Symmetry
por: Zheng, Han, et al.
Publicado: (2022)
por: Zheng, Han, et al.
Publicado: (2022)
Resource-efficient equivariant quantum convolutional neural networks
por: Chinzei, Koki, et al.
Publicado: (2024)
por: Chinzei, Koki, et al.
Publicado: (2024)
Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
por: Cunha, Gilberto, et al.
Publicado: (2025)
por: Cunha, Gilberto, et al.
Publicado: (2025)
Structured quantum learning via em algorithm for Boltzmann machines
por: Kimura, Takeshi, et al.
Publicado: (2025)
por: Kimura, Takeshi, et al.
Publicado: (2025)
Generative modeling assisted simulation of measurement-altered quantum criticality
por: Zhu, Yuchen, et al.
Publicado: (2024)
por: Zhu, Yuchen, et al.
Publicado: (2024)
Neural auto-designer for enhanced quantum kernels
por: Lei, Cong, et al.
Publicado: (2024)
por: Lei, Cong, et al.
Publicado: (2024)
Ejemplares similares
-
Towards provably efficient quantum algorithms for large-scale machine-learning models
por: Liu, Junyu, et al.
Publicado: (2023) -
Stochastic noise can be helpful for variational quantum algorithms
por: Liu, Junyu, et al.
Publicado: (2022) -
Optimal algorithmic complexity of inference in quantum kernel methods
por: Gil-Fuster, Elies, et al.
Publicado: (2026) -
Generative quantum machine learning via denoising diffusion probabilistic models
por: Zhang, Bingzhi, et al.
Publicado: (2023) -
A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
por: Wang, Yunfei, et al.
Publicado: (2024)