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Found 6 papers, 4 papers with code
A Review of Uncertainty for Deep Reinforcement Learning
While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed.
An Empirical Review of Optimization Techniques for Quantum Variational Circuits
Although a variety of optimization algorithms are employed in practice, there is often a lack of theoretical or empirical motivations for this choice.
Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices.
In Defense of the Paper
In this work, we argue that the root cause of hindrances in the accessibility of machine learning research lies not in the paper workflow but within the misaligned incentives behind the publishing and research processes.
Reinforcement Learning with Quantum Variational Circuits
This work explores the potential for quantum computing to facilitate reinforcement learning problems.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
