69% cos have adopted or plan to adopt Quantum Computing in future

69% cos have adopted or plan to adopt Quantum Computing in future
10 Jan, 2022

A new survey by Massachusetts-based Quantum Computing company Zapata has revealed that 69% of enterprises have adopted or are planning to adopt quantum computing technologies or solutions in the near future. 

The December 2021 report showed that Quantum Computing has moved from the fringes of technology to the shortlisted transformational technologies that could prove to provide the edge for enterprises in the next two years. 

The survey took inputs from 300 leaders at large global enterprises who has estimated revenues of over $250 million, with computing budgets over $1million. 

74% of the respondents said that those who fail to adopt quantum solutions will be left behind, while only 31% currently are not inclined towards Quantum Computing. However, only 29% have currently adopted quantum in some form or the other. 

Also read: India’s first Quantum Computing Tech Park to come up in Gujara

About 12% of early and advanced quantum adopters expect to achieve some form of competitive advantage within the next year, while 41% are expecting to gain an edge over the next two years. 

The main driving motivator behind the adoption of quantum is that it is poised to deliver better business performance and results, according to 60% of respondents who have already invested in quantum or are planning to over the next year. 

The report showed that the most advanced quantum adopters are 70% more likely than their enterprise counterparts to invest in quantum for the purpose of workforce development. These are the companies that have invested more than $1 million into the technology and are also 50% more likely to create more IPs related to quantum than others.

Machine learning and data analytics problems were the top use cases for early and advanced adopters of quantum, with 51% of worldwide companies having invested in ML and data analytics case studies.

“Areas where classical ML struggles — such as generative models in unsupervised and semi-supervised learning for augmenting datasets in predictive models — are better suited for quantum devices,” the report said.