Quantum Machine Learning and Bioinspired Quantum Technologies
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Abstract
There is no doubt artificial intelligence and machine learning have been significantly modifying the scientific and technological landscape, as well as society at large, over the last decade. Search engines, autonomous cars, language and speech translators, as well as image classifiers are only some possibilities this discipline enables and there is certainly more to come in the near future. Significant research efforts have been dedicated in the direction of quantum computing, a topic that has been receiving ever-growing attention from governmental institutions, multinational companies, and academics. A scalable quantum computer could offer unprecedented computational capabilities, enabling a revolution in information processing and transmission. Among the various approaches, quantum machine learning has emerged as an utterly successfulmethod to better control and understand quantum systems, benefitting from machine learning algorithms, while relying on quantum processors to accelerate machine learning tasks. When coupled with biomimetics, an area of science that aims at imitating biological systems in order to design more efficient artificial devices, quantum biomimetics arises as a novel field that bridges the gap between natural selection in bio systems and engineering processes in quantum devices with one ultimate goal: The replication of biological behaviors in quantum controllable systems, what we call “bioinspired quantum technologies”. The goal of this Special Issue is to present selective work at this interplay of quantummachine learning and quantum biomimetics. Among the collection of articles, we have works more oriented to quantum machine learning, including