Journal
ADVANCED QUANTUM TECHNOLOGIES
Volume 2, Issue 7-8, Pages -Publisher
WILEY
DOI: 10.1002/qute.201800065
Keywords
quantum adders; quantum artificial intelligence; quantum autoencoders; quantum machine learning
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Funding
- Spanish MINECO/FEDER [FIS2015-69983-P]
- Ramon y Cajal Grant [RYC-2012-11391]
- Basque Government [IT986-16]
- project OpenSuperQ of the EU Flagship on Quantum Technologies [820363]
- project QMiCS of the EU Flagship on Quantum Technologies [820505]
- U.S. Department of Energy, Office of Science, Office of Advance Scientific Computing Research (ASCR) [ERKJ335]
- NSFC [11474193]
- Shuguang Program [14SG35]
- program of Shanghai Municipal Science and Technology Commission [18010500400, 18ZR1415500]
- Program for Eastern Scholar
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Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. Recently, it is proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, the experimental implementation of this proposal in the Rigetti cloud quantum computer is carried out employing up to three qubits. The experimental fidelities are in good agreement with the theoretical prediction, thus proving the feasibility to realize quantum autoencoders via quantum adders in state-of-the-art superconducting quantum technologies.
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