Saved in:
| Main Authors: | Yang, Junhong, Wang, Banghai, Quan, Junyu, Li, Qin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.13713 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Stochastic noise can be helpful for variational quantum algorithms
by: Liu, Junyu, et al.
Published: (2022)
by: Liu, Junyu, et al.
Published: (2022)
Learning quantum symmetries with interactive quantum-classical variational algorithms
by: Lu, Jonathan Z., et al.
Published: (2022)
by: Lu, Jonathan Z., et al.
Published: (2022)
Towards efficient quantum algorithms for diffusion probabilistic models
by: Wang, Yunfei, et al.
Published: (2025)
by: Wang, Yunfei, et al.
Published: (2025)
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
by: Bhowmick, Rahul, et al.
Published: (2025)
by: Bhowmick, Rahul, et al.
Published: (2025)
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)
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
by: Kundu, Akash
Published: (2024)
by: Kundu, Akash
Published: (2024)
On the explainability of quantum neural networks based on variational quantum circuits
by: Daskin, Ammar
Published: (2023)
by: Daskin, Ammar
Published: (2023)
A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud
by: Liu, Wenjie, et al.
Published: (2023)
by: Liu, Wenjie, et al.
Published: (2023)
Estimating truncation effects of quantum bosonic systems using sampling algorithms
by: Hanada, Masanori, et al.
Published: (2022)
by: Hanada, Masanori, et al.
Published: (2022)
Generative flow-based warm start of the variational quantum eigensolver
by: Zou, Hang, et al.
Published: (2025)
by: Zou, Hang, et al.
Published: (2025)
Post-variational quantum neural networks
by: Huang, Po-Wei, et al.
Published: (2023)
by: Huang, Po-Wei, et al.
Published: (2023)
Non-variational supervised quantum kernel methods: a review
by: Tanner, John, et al.
Published: (2026)
by: Tanner, John, et al.
Published: (2026)
Deep learning-based variational autoencoder for classification of quantum and classical states of light
by: Bhupati, Mahesh, et al.
Published: (2024)
by: Bhupati, Mahesh, et al.
Published: (2024)
Explainable quantum regression algorithm with encoded data structure
by: Wang, C. -C. Joseph, et al.
Published: (2023)
by: Wang, C. -C. Joseph, et al.
Published: (2023)
The curse of random quantum data
by: Zhang, Kaining, et al.
Published: (2024)
by: Zhang, Kaining, et al.
Published: (2024)
Clustering by Contour coreset and variational quantum eigensolver
by: Yung, Canaan, et al.
Published: (2023)
by: Yung, Canaan, et al.
Published: (2023)
Fundamental causal bounds of quantum random access memories
by: Wang, Yunfei, et al.
Published: (2023)
by: Wang, Yunfei, et al.
Published: (2023)
Des-q: a quantum algorithm to provably speedup retraining of decision trees
by: Kumar, Niraj, et al.
Published: (2023)
by: Kumar, Niraj, et al.
Published: (2023)
Optimal algorithmic complexity of inference in quantum kernel methods
by: Gil-Fuster, Elies, et al.
Published: (2026)
by: Gil-Fuster, Elies, et al.
Published: (2026)
Quantum-data-driven dynamical transition in quantum learning
by: Zhang, Bingzhi, et al.
Published: (2024)
by: Zhang, Bingzhi, et al.
Published: (2024)
Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy
by: Tu, Yue, et al.
Published: (2025)
by: Tu, Yue, et al.
Published: (2025)
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)
Energy-dependent barren plateau in bosonic variational quantum circuits
by: Zhang, Bingzhi, et al.
Published: (2023)
by: Zhang, Bingzhi, et al.
Published: (2023)
Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
by: Cunha, Gilberto, et al.
Published: (2025)
by: Cunha, Gilberto, et al.
Published: (2025)
Structured quantum learning via em algorithm for Boltzmann machines
by: Kimura, Takeshi, et al.
Published: (2025)
by: Kimura, Takeshi, et al.
Published: (2025)
Minimizing classical resources in variational measurement-based quantum computation for generative modeling
by: Majumder, Arunava, et al.
Published: (2026)
by: Majumder, Arunava, et al.
Published: (2026)
A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
by: Wang, Yunfei, et al.
Published: (2024)
by: Wang, Yunfei, et al.
Published: (2024)
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)
QGAN-based data augmentation for hybrid quantum-classical neural networks
by: He, Run-Ze, et al.
Published: (2025)
by: He, Run-Ze, et al.
Published: (2025)
Dynamical transition in controllable quantum neural networks with large depth
by: Zhang, Bingzhi, et al.
Published: (2023)
by: Zhang, Bingzhi, et al.
Published: (2023)
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
by: Srimahajariyapong, Kasidit, et al.
Published: (2025)
by: Srimahajariyapong, Kasidit, et al.
Published: (2025)
Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
Intrinsic preservation of plasticity in continual quantum learning
by: Chen, Yu-Qin, et al.
Published: (2025)
by: Chen, Yu-Qin, et al.
Published: (2025)
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
by: East, Richard D. P., et al.
Published: (2023)
by: East, Richard D. P., et al.
Published: (2023)
Superior resilience to poisoning and amenability to unlearning in quantum machine learning
by: Chen, Yu-Qin, et al.
Published: (2025)
by: Chen, Yu-Qin, et al.
Published: (2025)
The Efficiency Frontier: Classical Shadows versus Quantum Footage
by: Ma, Shuowei, et al.
Published: (2025)
by: Ma, Shuowei, et al.
Published: (2025)
Enhancing variational quantum state diagonalization using reinforcement learning techniques
by: Kundu, Akash, et al.
Published: (2023)
by: Kundu, Akash, et al.
Published: (2023)
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
by: de Schoulepnikoff, Paulin, et al.
Published: (2025)
by: de Schoulepnikoff, Paulin, et al.
Published: (2025)
Training quantum machine learning models on cloud without uploading the data
by: He, Guang Ping
Published: (2024)
by: He, Guang Ping
Published: (2024)
Variational quantum and neural quantum states algorithms for the linear complementarity problem
by: De, Saibal, et al.
Published: (2025)
by: De, Saibal, et al.
Published: (2025)
Similar Items
-
Stochastic noise can be helpful for variational quantum algorithms
by: Liu, Junyu, et al.
Published: (2022) -
Learning quantum symmetries with interactive quantum-classical variational algorithms
by: Lu, Jonathan Z., et al.
Published: (2022) -
Towards efficient quantum algorithms for diffusion probabilistic models
by: Wang, Yunfei, et al.
Published: (2025) -
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
by: Bhowmick, Rahul, et al.
Published: (2025) -
Towards provably efficient quantum algorithms for large-scale machine-learning models
by: Liu, Junyu, et al.
Published: (2023)