Year: 2021
Type: article
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.
Source: npj Quantum Information
Authors: Keren Li, Shijie Wei, Pan Gao, Feihao Zhang, Zengrong Zhou, Tao Xin, Xiaoting Wang, Patrick Rebentrost, Guilu Long
Institutions: State Key Laboratory of Low-Dimensional Quantum Physics, Tsinghua University, Beijing Academy of Quantum Information Sciences, Southern University of Science and Technology, University of Electronic Science and Technology of China, Centre for Quantum Technologies, National University of Singapore, Beijing National Research Center for Information Science and Technology, Frontier Science Center for Quantum Information of the Ministry of Education of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,790
Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Beijing Innovation Center for Future Chip, National Key Research and Development Program of China, Special Project for Research and Development in Key areas of Guangdong Province, Basic and Applied Basic Research Foundation of Guangdong Province
Awards: G1654887313, G2038365503, G2299877239, G272681240, G3504089951, 核磁共振多态相体系的开展研究及其量子纠缠测量, G3771705337, G3794144889, G3886128100, G4336685212, G4344798175, G4518114676, G5252361160, 测量设备无关的量子安全直接通信理论与实验研究, G6837542917, G6869965316, G7399362544, G76247503, G7731819942, G7933946904, G8264566704, G957826975
OpenAlex ID: W2798037622