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| DOI：https://doi.org/10.1007/s11768-019-8289-0 |
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| New directions in quantum neural networks research |
| Wei CUI,Shilu YAN |
| (School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China) |
| Abstract: |
| With the development of quantum information technology, the performance highlighted by quantum computing represents the unparalleled superiority over the classical computing. As a new computing paradigm, it has attracted more and more attentions. Quantum theory and artificial neural networks have many essential intercommunication in solving problems. The fusion of these two fields forms the discipline of quantum neural networks [1, 2], meanwhile, provides many research opportunities. Firstly, a number of recent works have used neural networks to study properties of quantum systems. Secondly, redefining and reconstructing more efficient neural networks based on
quantum information theory have gradually aroused people’s interest. In the former aspect, there are many complicated problems in quantum many body systems [3]. These problems often transcend the processing power of traditional exhaustive methods and logical programming methods. For example, simulating waves functions of quantum systems, identifying different quantum phases of matter, classifying different quantum states, and so on. In the general quantum many body system, the Hilbert space dimension will
increase exponentially to the system scale [3], which bring essential difficulties to the description and calculation of many body wave functions of the quantum correlated systems, especially the strongly correlated system [1]. As the neural network models show the ability to represent complex multivariable functions, it can be used to represent the association and entanglement of quantum many body wave functions, and then calculate the wave functions generated by ground state [4, 5] or quantum dynamics evolution [6–8].... |
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