Quantum Machine Learning and Bioinspired Quantum Technologies

Advanced Quantum TechnologiesVolume 2, Issue 7-8 1900075 EditorialFree Access Quantum Machine Learning and Bioinspired Quantum Technologies Lucas Lamata, Lucas Lamata lucas.lamata@gmail.com Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla, SpainSearch for more papers by this authorMikel Sanz, Mikel Sanz mikel.sanz@ehu.es Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, SpainSearch for more papers by this authorEnrique Solano, Enrique Solano enr.solano@gmail.com Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, ChinaSearch for more papers by this author Lucas Lamata, Lucas Lamata lucas.lamata@gmail.com Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla, SpainSearch for more papers by this authorMikel Sanz, Mikel Sanz mikel.sanz@ehu.es Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, SpainSearch for more papers by this authorEnrique Solano, Enrique Solano enr.solano@gmail.com Quantum Technologies for Information Science (QUTIS), Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, ChinaSearch for more papers by this author First published: 06 August 2019 https://doi.org/10.1002/qute.201900075Citations: 3AboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 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 successful method 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 quantum machine learning and quantum biomimetics. Among the collection of articles, we have works more oriented to quantum machine learning, including - The use of the first quantum Ansätze for the statistical relational learning on knowledge graphs using parametric quantum circuits, by Yunpu Ma et al.1 - The analysis of the nonlinear Schrödinger equation to store information analogously as with a Hopfield's associative memory, by Alexey N. Pyrkov and co-workers.2 - Experimentally carrying out a quantum autoencoder based on quantum adders with the Rigetti cloud quantum computer, by Yongcheng Ding et al.3 - A quantum experiment to reconstruct an unknown photonic quantum state with a limited amount of copies, in the context of reinforcement learning, by Shang Yu et al.4 - A prediction of the band gap which represents one of the basic properties of a crystalline material via machine learning calculations, by Alexander V. Balatsky and co-workers.5 - A Review Article on the progress in using artificial neural networks to build quantum many-body states, by Zhih-Ahn Jia et al.6 In the quantum biomimetic field, we have - Contributions related to quantum synchronization via quantum machine learning, in the parallel works by Francisco A. Cárdenas-López et al.7 and Gabriel Garau Estarellas et al.8 - An article motivating the debate on possible quantum effects in conscious minds, by Göran Wendin.9 We believe that this Special Issue will shed light on unexplored areas up to now, acting as the precursor for further exciting research in the field of bioinspired quantum technologies. References 1Y. Ma, V. Tresp, L. Zhao, Y. Wang, Adv. Quantum Technol. 2019, 2, 1800078. 2V. V. Cherny, T. Byrnes, A. N. Pyrkov, Adv. Quantum Technol. 2019, 2, 1800087. 3Y. Ding, L. Lamata, M. Sanz, X. Chen, E. Solano, Adv. Quantum Technol. 2019, 2, 1800065. 4S. Yu, F. Albarrán-Arriagada, J. C. Retamal, Y.-T. Wang, W. Liu, Z.-J. Ke, Y. Meng, Z.-P. Li, J.-S. Tang, E. Solano, L. Lamata, C.-F. Li, G.-C. Guo, Adv. Quantum Technol. 2019, 2, 1800074. 5B. Olsthoorn, R. M. Geilhufe, S. S. Borysov, A. V. Balatsky, Adv. Quantum Technol. 2019, 2, 1900023. 6Z.-A. Jia, B. Yi, R. Zhai, Y.-C. Wu, G.-C. Guo, G.-P. Guo, Adv. Quantum Technol. 2019, 2, 1800077. 7F. A. Cárdenas-López, M. Sanz, J. C. Retamal, E. Solano, Adv. Quantum Technol. 2019, 2, 1800076. 8G. Garau Estarellas, G. L. Giorgi, M. C. Soriano, R. Zambrini, Adv. Quantum Technol. 2019, 2, 1800085. 9G. Wendin, Adv. Quantum Technol. 2019, 2, 1800081. Citing Literature Volume2, Issue7-8Special Issue: Quantum Machine LearningAugust 20191900075 ReferencesRelatedInformation