Modeling quantum neural network gradient with reinforcement learning
quant-ph, cs.ET, cs.NE
Submitted: 2026-09-25
Updated: 2026-09-25
Terminology
Sources
- Learning to learn with quantum neural networks via classical neural networks
- Reinforcement Learning for Variational Quantum Circuits Design
- TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture search
- Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
- Quantum circuit optimization with deep reinforcement learning
- Quantum Architecture Search via Deep Reinforcement Learning
- Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
- Efficient calculation of gradients in classical simulations of variational quantum algorithms
- PennyLane: Automatic differentiation of hybrid quantum-classical computations
- Adam: A Method for Stochastic Optimization
- SGDR: Stochastic Gradient Descent with Warm Restarts
- A Unified Theory of Quantum Neural Network Loss Landscapes
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- The CMA Evolution Strategy: A Tutorial
- Hardware-efficient ansatz without barren plateaus in any depth
- Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
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