Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks-Reference-Cited by-同舟云学术

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Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks

Published:2022-03-14 Issue:3 Volume:4 Page:269-278
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Chen Yu-Qin,Chen Yu,Lee Chee-Kong,Zhang Shengyu,Hsieh Chang-Yu

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-022-00446-y.pdf

Reference71 articles.

1. Jiang, S., Britt, K. A., McCaskey, A. J., Humble, T. S. & Kais, S. Quantum annealing for prime factorization. Sci. Rep. 8, 17667 (2018).

2. King, A. D. et al. Observation of topological phenomena in a programmable lattice of 1,800 qubits. Nature 560, 456–460 (2018).

3. Harris, R. et al. Phase transitions in a programmable quantum spin glass simulator. Science 361, 162–165 (2018).

4. Willsch, D., Willsch, M., De Raedt, H. & Michielsen, K. Support vector machines on the D-wave quantum annealer. Comput. Phys. Commun. 248, 107006 (2020).

5. Mott, A., Job, J., Vlimant, Jean-Roch, Lidar, D. & Spiropulu, M. Solving a Higgs optimization problem with quantum annealing for machine learning. Nature 550, 375–379 (2017).

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