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Title: Optimizing a polynomial function on a quantum processor
Authors: Li, Keren
Wei, Shijie
Gao, Pan
Zhang, Feihao
Zhou, Zengrong
Xin, Tao
Wang, Xiaoting
Rebentrost, Patrick 
Long, Guilu
Issue Date: 29-Jan-2021
Publisher: Nature Research
Citation: Li, Keren, Wei, Shijie, Gao, Pan, Zhang, Feihao, Zhou, Zengrong, Xin, Tao, Wang, Xiaoting, Rebentrost, Patrick, Long, Guilu (2021-01-29). Optimizing a polynomial function on a quantum processor. npj Quantum Information 7 (1) : 16. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest descent. Since for high-dimensional problems the required computational resources can be prohibitive, it is desirable to investigate quantum versions of the gradient descent, such as the recently proposed (Rebentrost et al.1). Here, we develop this protocol and implement it on a quantum processor with limited resources. A prototypical experiment is shown with a four-qubit nuclear magnetic resonance quantum processor, which demonstrates the iterative optimization process. Experimentally, the final point converged to the local minimum with a fidelity >94%, quantified via full-state tomography. Moreover, our method can be employed to a multidimensional scaling problem, showing the potential to outperform its classical counterparts. Considering the ongoing efforts in quantum information and data science, our work may provide a faster approach to solving high-dimensional optimization problems and a subroutine for future practical quantum computers. © 2021, The Author(s).
Source Title: npj Quantum Information
ISSN: 2056-6387
DOI: 10.1038/s41534-020-00351-5
Rights: Attribution 4.0 International
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