Trainability issues in quantum policy gradients
Fuente:
arXiv
Saved in:
| Main Authors: | Sequeira, André, Santos, Luis Paulo, Barbosa, Luis Soares |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
On Quantum Natural Policy Gradients
by: Sequeira, André, et al.
Published: (2024)
by: Sequeira, André, et al.
Published: (2024)
Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
by: Cunha, Gilberto, et al.
Published: (2025)
by: Cunha, Gilberto, et al.
Published: (2025)
A hybrid classical-quantum approach to highly constrained Unit Commitment problems
by: Salgado, Bruna, et al.
Published: (2024)
by: Salgado, Bruna, 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)
Trainability Beyond Linearity in Variational Quantum Objectives
by: Ma, Gordon, et al.
Published: (2026)
by: Ma, Gordon, et al.
Published: (2026)
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 the Trainability of Variational Quantum Circuits with Regularization Strategies
by: Zhuang, Jun, et al.
Published: (2024)
by: Zhuang, Jun, 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 Circuit Trainability with Selective Gate Activation Strategy
by: Cho, Jeihee, et al.
Published: (2025)
by: Cho, Jeihee, et al.
Published: (2025)
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four
by: Santos, Filipe, et al.
Published: (2025)
by: Santos, Filipe, et al.
Published: (2025)
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
by: Shen, Kevin, et al.
Published: (2026)
by: Shen, Kevin, et al.
Published: (2026)
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)
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)
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)
Sequential learning on a Tensor Network Born machine with Trainable Token Embedding
by: Hou, Wanda, et al.
Published: (2023)
by: Hou, Wanda, et al.
Published: (2023)
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)
A Laplacian-based Quantum Graph Neural Network for Semi-Supervised Learning
by: Gholipour, Hamed, et al.
Published: (2024)
by: Gholipour, Hamed, 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)
Learning to rank quantum circuits for hardware-optimized performance enhancement
by: Hartnett, Gavin S., et al.
Published: (2024)
by: Hartnett, Gavin S., et al.
Published: (2024)
Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
by: Mancilla, Javier, et al.
Published: (2024)
by: Mancilla, Javier, et al.
Published: (2024)
Exact gradients for linear optics with single photons
by: Facelli, Giorgio, et al.
Published: (2024)
by: Facelli, Giorgio, et al.
Published: (2024)
Natural gradient and parameter estimation for quantum Boltzmann machines
by: Patel, Dhrumil, et al.
Published: (2024)
by: Patel, Dhrumil, et al.
Published: (2024)
Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
by: Hamid, Eyad I. B.
Published: (2026)
by: Hamid, Eyad I. B.
Published: (2026)
Computing the gradients with respect to all parameters of a quantum neural network using a single circuit
by: He, Guang Ping
Published: (2023)
by: He, Guang Ping
Published: (2023)
Generative quantum advantage for classical and quantum problems
by: Huang, Hsin-Yuan, et al.
Published: (2025)
by: Huang, Hsin-Yuan, et al.
Published: (2025)
Provable and scalable quantum Gaussian processes for quantum learning
by: Jäger, Jonas, et al.
Published: (2026)
by: Jäger, Jonas, et al.
Published: (2026)
Efficient quantum-enhanced classical simulation for patches of quantum landscapes
by: Lerch, Sacha, et al.
Published: (2024)
by: Lerch, Sacha, et al.
Published: (2024)
Neural networks leverage nominally quantum and post-quantum representations
by: Riechers, Paul M., et al.
Published: (2025)
by: Riechers, Paul M., et al.
Published: (2025)
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)
On exploring the potential of quantum auto-encoder for learning quantum systems
by: Du, Yuxuan, et al.
Published: (2021)
by: Du, Yuxuan, et al.
Published: (2021)
On the explainability of quantum neural networks based on variational quantum circuits
by: Daskin, Ammar
Published: (2023)
by: Daskin, Ammar
Published: (2023)
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)
Re-uploading quantum data: A universal function approximator for quantum inputs
by: Cha, Hyunho, et al.
Published: (2025)
by: Cha, Hyunho, et al.
Published: (2025)
Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor
by: Zhang, Hai-Feng, et al.
Published: (2025)
by: Zhang, Hai-Feng, et al.
Published: (2025)
Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
by: Wadhwa, Harsh, et al.
Published: (2026)
by: Wadhwa, Harsh, et al.
Published: (2026)
Online learning of quantum processes
by: Raza, Asad, et al.
Published: (2024)
by: Raza, Asad, et al.
Published: (2024)
Similar Items
-
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
by: Coelho, Rodrigo, et al.
Published: (2024) -
On Quantum Natural Policy Gradients
by: Sequeira, André, et al.
Published: (2024) -
Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
by: Cunha, Gilberto, et al.
Published: (2025) -
A hybrid classical-quantum approach to highly constrained Unit Commitment problems
by: Salgado, Bruna, et al.
Published: (2024) -
Trainability barriers and opportunities in quantum generative modeling
by: Rudolph, Manuel S., et al.
Published: (2023)