The deep learning algorithms include supervised. unsupervised and reinforcement learning approaches. In the years they have successfully been applied to many fields, ranging from predicting the sequence specificities off DNA- and RNA-binding proteins to design of novel materials. The reasons of the success consists of its ability to create a hierarchy of representations across a deep neural network. Such layered decoding allows to extract key features from a complex parameter space, and next to recognize, generalize, apply them, depending on the specific task. The advent of quantum computing has boosted the research around quantum machine learning algorihtms. Indeed, some of the algorithms have a quantum counterpart. Despite the relatively small resources of quantum computers, there is a number of algorithms that can be tested. BOth circuital quantum computer and adiabatic quantum computer may successfully host quantum machine learning algorithms. I review a number of algorithms that have been implemented, the current perspectives for what concerns the scaling law trend for quantum computers, and the consequences of achieving sufficient harwdware resoorces so to treat problems of the same degree of complexity than those currently managed by high power computing.<br/>

Deep learning and quantum machine learning: Applications in life and materials science

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.