Five Lessons From AI On Closing Quantum’s Talent Gap Before It’s Too Late

Five Lessons From AI On Closing Quantum’s Talent Gap Before It’s Too Late
Five Lessons From AI On Closing Quantum’s Talent Gap Before It’s Too Late
To ensure organizations have access to the quantum computing talent they need when they need it, leaders will have to upskill workers and create pathways for new talent, just as they did for AI.
When AI emerged from its long winter and sprung onto business agendas in the 2010s, a scarcity of data science talent put considerable constraints on how and where business leaders could apply the technology. While AI talent challenges remain, strides have been made and many lessons have been learned that can be applied to tech talent strategies overall.
An arguably wider talent gap in quantum technology threatens to stall progress on breakthrough quantum use cases, jeopardizing the creation of a massive amount of business value. Quantum computing alone, which represents the largest market potential for the three mains areas of quantum technology (the other two areas being quantum sensing and quantum communications),1 could account for up to nearly $700 billion in value.2
Our research finds that there is only one qualified quantum candidate available for every three quantum job openings (Exhibit 1).3 By 2025, we predict that less than 50 percent of quantum computing jobs will be filled unless significant interventions occur.
The talent gap in quantum technology is wide, but interventions such as upskilling experts in adjacent fields can begin to fill it.

Quantum start-ups and established tech companies—especially those in the hardware market, where more than half of today’s quantum investments are concentrated4 —have been the first to wrestle with this talent crunch as they race to solve fundamental questions in the field and deliver fault-tolerant quantum systems, which are necessary to unlock the technology’s full potential.

It’s still early days, with many unknowns, but the technology is rapidly progressing. With this progress, the demand for quantum talent is shifting, first to software companies and then to enterprises that will use the technology. Leaders across industries are already beginning to assemble quantum teams and test early-stage algorithms on the current class of quantum systems.

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This includes, for example, exploring how quantum algorithms can improve encryption protocols in financial services, optimize routes and fleets in logistics, and improve clinical-trial site selection in pharmaceuticals.

As we saw with AI, the growth of graduate programs in the field will be one necessary development to ensure a robust talent pipeline. Quantum graduate programs are still not widely available or accessible at the university level. Today, of the 176 quantum research programs at universities worldwide, only 29 provide graduate-level degrees in the subject.5

But beyond this, many of the same strategies and investments that companies have used to successfully build their AI teams, such as upskilling workers and creating pathways for new talent, can also serve them well when building quantum teams.

Based on data and insights from our research and work in the field,6 along with the experience of The Coding School’s quantum computing education initiative, Qubit by Qubit, there are five lessons from the AI talent journey that can help organizations build the quantum talent they need so they’re ready to capture value as the technology comes of age.