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Chinese search engine giant Baidu to donate quantum computing facility to state-backed lab in similar move to Alibaba

  • Last March, Baidu and BAQIS jointly launched the country’s first quantum computing intellectual property alliance, with the goal of facilitating easier IP cross-licensing
  • In 2023, Alibaba’s in-house research institution Damo Academy announced it was donating its own quantum lab to Zhejiang University

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The company logo on a building at the Baidu Technology Park in Beijing, August 21, 2023. Photo: Bloomberg
Ben Jiangin Beijing

Chinese search engine giant Baidu is donating its quantum computing research facility to the Beijing Academy of Quantum Information Sciences (BAQIS), in a move similar to one made last year by e-commerce giant Alibaba Group Holding, as both firms lock horns in the country’s artificial intelligence (AI) race.

Baidu said on Thursday it would donate its Baidu Institute for Quantum Computing and associated experiment apparatus to BAQIS, a government-backed laboratory. The two parties are currently discussing details of the donation, according to a company statement.

Beijing-based Baidu, which was an early Chinese investor in AI applications including autonomous driving, established its quantum computing facility in early 2018.

It was launched with a mission to “become a top-tier global quantum computing research institution in five years”, with a focus on several areas including quantum AI, quantum algorithms and quantum architecture, according to local media the Paper.

In March last year, Baidu and BAQIS jointly launched the country’s first quantum computing intellectual property (IP) alliance, with the goal of facilitating easier IP cross-licensing and innovation in the industry.

Quantum computing is an emerging field of science that harnesses the principles of fundamental physics to solve problems too complex for traditional computers.

While quantum computers are not designed to replace traditional computers, they are able to solve very complex statistical problems via simultaneous multiple calculations, making them much faster.

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