Quantum Neural Networks Achieving Quantum Algorithms
This paper explores the possibility to construct quantum algorithms by means of neural networks endowed with quantum gates evolved to achieve prescribed goals. First tentatives are performed on the well known Deutsch and Deutsch-Jozsa problems. Results are promising as solutions are detected for different sizes and initializations of the problems using a standard evolutionary learning process. This approach is then used to design quantum operators by combining simple quantum operators belonging to a predefined set.
We thank Andrea Roli for his critical reading of a preliminary version of the paper.
This research used computational resources of the “Plateforme Technologique de Calcul Intensif (PTCI)” located at the University of Namur, Belgium, which is supported by the F.R.S.-FNRS.
This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimisation), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its authors.
- 4.Deutsch, D.: Quantum computational networks. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 425, pp. 73–90. The Royal Society (1989). https://doi.org/10.1098/rspa.1989.0099
- 8.Mermin, D.: Calculs et algorithmes quantiques: méthodes et exemples. EDP Sciences, Les Ulis (2010)Google Scholar