Shulou(Shulou.com)11/24 Report--

CTOnews.com, November 22, according to Foxconn Group, Xie Mingxiu, director of the Institute of Quantum Computing at Hon Hai Research Institute and a team from Sydney University in Australia, jointly submitted a "Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits" research report to propose a solution to the long-standing barren plateau phenomenon (Barren Plateaus) in this field, which can be improved by giving appropriate initial values of adjustable parameters.

This research has been accepted by the world's top machine learning and computational neuroscience conference: neural Information processing Systems Conference (NeurIPS 2022). It stood out among more than 10, 000 submissions worldwide and was published at the end of November.

CTOnews.com learned that the barren plateau means that when the number of bits of a quantum computer is large, the framework of the current quantum neural network can easily become unable to train effectively, and its objective function will become very flat, resulting in training for too long or failure.

In response to the research results, Director Xie Mingxiu said that a solution to the barren plateau phenomenon is proposed to enable quantum learning machines to show real advantages over traditional machines. Combined with this scheme, the Institute of Quantum Computing presented the research results of quantum simulation in battery development on Foxconn Day (HHTD22) this year, which greatly reduced the required quantum resources.

Speaking of the barren plateau phenomenon, Director Xie Mingxiu further pointed out that generally speaking, in the process of quantum machine learning, we can obtain the desired quantum circuit model by controlling the adjustable parameters of logic gates, but in the process of learning, it is often difficult to update parameters because of too many logic gates and too deep structure.

Director Xie Mingxiu said that by properly giving the initial values of adjustable parameters, we have improved the barren plateau phenomenon and solved the problems that have plagued the field of quantum machine learning for a long time, and research in this field has made a breakthrough.

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