Journal
ADVANCED QUANTUM TECHNOLOGIES
Volume 2, Issue 7-8, Pages -Publisher
WILEY
DOI: 10.1002/qute.201800074
Keywords
quantum optics; quantum state reconstruction; reinforcement learning
Categories
Funding
- National Key Research and Development Program of China [2017YFA0304100]
- National Natural Science Foundation of China [61327901, 11674304, 11822408, 61490711, 11774335, 11821404]
- Key Research Program of Frontier Sciences of the Chinese Academy of Sciences [QYZDY-SSW-SLH003]
- Youth Innovation Promotion Association of Chinese Academy of Sciences [2017492]
- Foundation for Scientific Instrument and Equipment Development of Chinese Academy of Sciences [YJKYYQ20170032]
- Anhui Initiative in Quantum Information Technologies [AHY020100, AHY060300]
- National Postdoctoral Program for Innovative Talents [BX20180293]
- China Postdoctoral Science Foundation [2018M640587]
- Fundamental Research Funds for the Central Universities [WK2470000026]
- Centro Basal [FB0807]
- Ramon y Cajal Grant [RYC-2012-11391]
- MINECO/FEDER [FIS2015-69983-P]
- Basque Government [IT986-16]
- project OpenSuperQ of the EU Flagship on Quantum Technologies [820363]
- project QMiCS of the EU Flagship on Quantum Technologies [820505]
Ask authors/readers for more resources
An experiment is performed to reconstruct an unknown photonic quantum state with a limited amount of copies. A semiquantum reinforcement learning approach is employed to adapt one qubit state, an agent, to an unknown quantum state, an environment, by successive single-shot measurements and feedback, in order to achieve maximum overlap. The experimental learning device herein, composed of a quantum photonics setup, can adjust the corresponding parameters to rotate the agent system based on the measurement outcomes 0 or 1 in the environment (i.e., reward/punishment signals). The results show that, when assisted by such a quantum machine learning technique, fidelities of the deterministic single-photon agent states can achieve over 88% under a proper reward/punishment ratio within 50 iterations. This protocol offers a tool for reconstructing an unknown quantum state when only limited copies are provided, and can also be extended to higher dimensions, multipartite, and mixed quantum state scenarios.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available