Water loss and the coincident increase in membrane resistance to proton conduction are significant barriers to high performance operation of traditional proton exchange membrane fuel cells at elevated temperatures where the relative humidity may be reduced. We report here approaches to the development of high temperature membranes for proton exchange membrane fuel cells; composite perfluorinated sulfonic acid membranes were prepared to improve water retention, and non-aqueous proton conducting membranes were prepared to circumvent the loss of water. Experimental results of composite membranes of Nafion and zirconium phosphate show improved operation at elevated temperatures. Imidazole impregnated membranes poisoned the electrocatalysts. Cesium hydrogen sulfate membranes were not able to produce appreciable current. A brief analysis of temperature requirements for CO tolerance and a framework for understanding water loss from fuel cell membranes are presented. #
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.