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A hybrid quantum-classical Hamiltonian learning algorithm

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Conclusion

This study develops a concrete near-term quantum algorithm for Hamiltonian learning and demonstrates its effectiveness. In particular, we show that learning the spectrum of Hamiltonians during the learning process could produce high-precision estimates of the target interaction coefficients. Our work may have applications in quantum device certification, quantum simulation, and quantum machine learning.

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References

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Acknowledgements

Youle WANG and Guangxi LI acknowledge support from Australian Research Council (Grant No. DP180100691) and Baidu-UTS AI Meets Quantum project. Guangxi LI acknowledges the financial support from China Scholarship Council (Grant No. 201806070139).

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Authors and Affiliations

  1. Center for Quantum Software and Information, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Youle Wang & Guangxi Li

  2. Baidu Research, Institute for Quantum Computing, Beijing, 100193, China

    Youle Wang, Guangxi Li & Xin Wang

Authors
  1. Youle Wang
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  2. Guangxi Li
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  3. Xin Wang
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Corresponding author

Correspondence to Xin Wang.

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Supporting information

The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Wang, Y., Li, G. & Wang, X. A hybrid quantum-classical Hamiltonian learning algorithm. Sci. China Inf. Sci. 66, 129502 (2023). https://doi.org/10.1007/s11432-021-3382-2

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  • DOI: https://doi.org/10.1007/s11432-021-3382-2

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