Sequential learning on a Tensor Network Born machine with Trainable Token Embedding
Fuente:
arXiv
Saved in:
| Main Authors: | Hou, Wanda, Li, Miao, You, Yi-Zhuang |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
by: Shen, Kevin, et al.
Published: (2026)
by: Shen, Kevin, et al.
Published: (2026)
Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies
by: Zhuang, Jun, et al.
Published: (2024)
by: Zhuang, Jun, et al.
Published: (2024)
Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
by: Murota, Keisuke, et al.
Published: (2025)
by: Murota, Keisuke, et al.
Published: (2025)
Regularized second-order optimization of tensor-network Born machines
by: Ben-Dov, Matan, et al.
Published: (2025)
by: Ben-Dov, Matan, 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)
Quantum-Classical Machine learning by Hybrid Tensor Networks
by: Liu, Ding, et al.
Published: (2020)
by: Liu, Ding, et al.
Published: (2020)
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)
Trainability issues in quantum policy gradients
by: Sequeira, André, et al.
Published: (2024)
by: Sequeira, André, et al.
Published: (2024)
Generative quantum machine learning via denoising diffusion probabilistic models
by: Zhang, Bingzhi, et al.
Published: (2023)
by: Zhang, Bingzhi, et al.
Published: (2023)
Tensor network to learn the wavefunction of data
by: Dymarsky, Anatoly, et al.
Published: (2021)
by: Dymarsky, Anatoly, et al.
Published: (2021)
Special-Unitary Parameterization for Trainable Variational Quantum Circuits
by: Chen, Kuan-Cheng, et al.
Published: (2025)
by: Chen, Kuan-Cheng, 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)
Enhancing Circuit Trainability with Selective Gate Activation Strategy
by: Cho, Jeihee, et al.
Published: (2025)
by: Cho, Jeihee, et al.
Published: (2025)
Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
by: Meng, Qingyu, et al.
Published: (2026)
by: Meng, Qingyu, et al.
Published: (2026)
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
by: Sadhu, Abhishek, et al.
Published: (2026)
by: Sadhu, Abhishek, et al.
Published: (2026)
Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
by: Chandrasekhar, Swathi, et al.
Published: (2025)
by: Chandrasekhar, Swathi, et al.
Published: (2025)
TensorKrowch: Smooth integration of tensor networks in machine learning
by: Monturiol, José Ramón Pareja, et al.
Published: (2023)
by: Monturiol, José Ramón Pareja, et al.
Published: (2023)
Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
by: Lopez-Piqueres, Javier, et al.
Published: (2024)
by: Lopez-Piqueres, Javier, et al.
Published: (2024)
Explaining Anomalies with Tensor Networks
by: Hohenfeld, Hans, et al.
Published: (2025)
by: Hohenfeld, Hans, et al.
Published: (2025)
Reinforcement learning-based architecture search for quantum machine learning
by: Rapp, Frederic, et al.
Published: (2024)
by: Rapp, Frederic, et al.
Published: (2024)
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)
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
by: Wang, Samson, et al.
Published: (2021)
by: Wang, Samson, et al.
Published: (2021)
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
by: Coelho, Rodrigo, et al.
Published: (2024)
by: Coelho, Rodrigo, et al.
Published: (2024)
Fourier series weight in quantum machine learning
by: Atchade-Adelomou, Parfait, et al.
Published: (2023)
by: Atchade-Adelomou, Parfait, et al.
Published: (2023)
Opportunities and limitations of explaining quantum machine learning
by: Gil-Fuster, Elies, et al.
Published: (2024)
by: Gil-Fuster, Elies, et al.
Published: (2024)
Tensor Network Estimation of Distribution Algorithms
by: Gardiner, John, et al.
Published: (2024)
by: Gardiner, John, et al.
Published: (2024)
Quantum Scrambling Born Machine
by: Płodzień, Marcin
Published: (2026)
by: Płodzień, Marcin
Published: (2026)
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
by: Poppel, Michael, et al.
Published: (2026)
by: Poppel, Michael, et al.
Published: (2026)
Can machine learning for quantum-gas experiments be explainable?
by: Zwolak, I. B. Spielman amd J. P.
Published: (2026)
by: Zwolak, I. B. Spielman amd J. P.
Published: (2026)
Entangling Machine Learning with Quantum Tensor Networks
by: van der Poel, Constantijn, et al.
Published: (2024)
by: van der Poel, Constantijn, et al.
Published: (2024)
Trainability barriers and opportunities in quantum generative modeling
by: Rudolph, Manuel S., et al.
Published: (2023)
by: Rudolph, Manuel S., 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)
A general learning scheme for classical and quantum Ising machines
by: Schmid, Ludwig, et al.
Published: (2023)
by: Schmid, Ludwig, et al.
Published: (2023)
Variational decision diagrams for quantum-inspired machine learning applications
by: Vargas-Calderón, Vladimir, et al.
Published: (2025)
by: Vargas-Calderón, Vladimir, et al.
Published: (2025)
Symmetry breaking in geometric quantum machine learning in the presence of noise
by: Tüysüz, Cenk, et al.
Published: (2024)
by: Tüysüz, Cenk, et al.
Published: (2024)
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)
Spectral methods: crucial for machine learning, natural for quantum computers?
by: Belis, Vasilis, et al.
Published: (2026)
by: Belis, Vasilis, et al.
Published: (2026)
Using matrix-product states for time-series machine learning
by: Moore, Joshua B., et al.
Published: (2024)
by: Moore, Joshua B., et al.
Published: (2024)
Benchmarking quantum machine learning kernel training for classification tasks
by: Alvarez-Estevez, Diego
Published: (2024)
by: Alvarez-Estevez, Diego
Published: (2024)
Dynamical simulation via quantum machine learning with provable generalization
by: Gibbs, Joe, et al.
Published: (2022)
by: Gibbs, Joe, et al.
Published: (2022)
Similar Items
-
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
by: Shen, Kevin, et al.
Published: (2026) -
Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies
by: Zhuang, Jun, et al.
Published: (2024) -
Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
by: Murota, Keisuke, et al.
Published: (2025) -
Regularized second-order optimization of tensor-network Born machines
by: Ben-Dov, Matan, et al.
Published: (2025) -
Trainability Beyond Linearity in Variational Quantum Objectives
by: Ma, Gordon, et al.
Published: (2026)