The US is investing $2 billion in the quantum computing industry.

The Trump administration plans to invest $2 billion in nine quantum computing companies, including a new joint venture with IBM, to boost America's technological leadership and compete with China in this strategic field.

According to an announcement by the US Department of Commerce on May 21, the investment will be made in the form of equity holdings in the businesses receiving the support.

The US is pursuing its ambition to lead the world in quantum technology. Photo: Reuters

IBM will receive $1 billion to establish Anderon, a quantum chip manufacturing company, in New York State, becoming the first dedicated quantum chip manufacturing facility in the United States.

Meanwhile, chipmaker GlobalFoundries will receive $375 million to build a factory in the US to manufacture components for quantum computing systems. The company will also establish a new division called Quantum Technology Solutions to focus on expanding quantum hardware production.

In addition, other quantum computing companies such as D-Wave, Rigetti Computing, and Infleqtion will also be allocated approximately $100 million each to develop their technology.

Diraq received up to $38 million to address the technical hurdles of quantum technology.

Following the announcement by the US government , shares of many quantum computing companies surged, with some rising by more than 30%.

The investment comes from incentives under the CHIPS and Science Act, signed into law by former President Joe Biden. This is the latest move by Washington to strengthen strategic technology supply chains and reduce dependence on China.

U.S. Commerce Secretary Howard Lutnick said: “These investments will boost domestic industry, create thousands of high-paying jobs, and enhance America’s quantum technology capabilities.”

Quantum computing is attracting significant investor interest due to its potential to accelerate fields such as drug research, financial modeling, and cryptography.

However, this technology still faces many challenges, particularly its high error rate and difficulties in large-scale commercialization.

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