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Scalable and Programmable Phononic Network with Trapped Ions

Authors :
Chen, Wentao
Lu, Yao
Zhang, Shuaining
Zhang, Kuan
Huang, Guanhao
Qiao, Mu
Su, Xiaolu
Zhang, Jialiang
Zhang, Jingning
Banchi, Leonardo
Kim, M. S.
Kim, Kihwan
Source :
Nature Physics, 2023: 1-7
Publication Year :
2022

Abstract

Controllable bosonic systems can provide post-classical computational power with sub-universal quantum computational capability. A network that consists of a number of bosons evolving through beam-splitters and phase-shifters between different modes, has been proposed and applied to demonstrate quantum advantages. While the network has been implemented mostly in optical systems with photons, recently alternative realizations have been explored, where major limitations in photonic systems such as photon loss, and probabilistic manipulation can be addressed. Phonons, the quantized excitations of vibrational modes, of trapped ions can be a promising candidate to realize the bosonic network. Here, we experimentally demonstrate a minimal-loss phononic network that can be programmed and in which any phononic states are deterministically prepared and detected. We realize the network with up to four collective-vibrational modes, which can be straightforwardly extended to reveal quantum advantage. We benchmark the performance of the network with an exemplary algorithm of tomography for arbitrary multi-mode states with a fixed total phonon number. We obtain reconstruction fidelities of 94.5 $\pm$ 1.95 % and 93.4 $\pm$ 3.15 % for single-phonon and two-phonon states, respectively. Our experiment demonstrates a clear and novel pathway to scale up a phononic network for various quantum information processing beyond the limitations of classical and other quantum systems.

Subjects

Subjects :
Quantum Physics

Details

Database :
arXiv
Journal :
Nature Physics, 2023: 1-7
Publication Type :
Report
Accession number :
edsarx.2207.06115
Document Type :
Working Paper
Full Text :
https://doi.org/10.1038/s41567-023-01952-5