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A long-term shortage of quantum information scientists?

20
May
,
2021
Yuval Boger

It was not too long ago that if you earned a Ph.D. in quantum physics, your job prospects would be quite limited. Now, there is an acute shortage of quantum physicists and quantum information scientists as companies race to form quantum computing teams.

Such is the high interest and strong demand that Harvard University recently launched a Ph.D. program in quantum science and engineering. Harvard president Larry Bacow notes that “[Harvard’s] faculty and students are driving progress that will reshape our world through quantum computing, networking, cryptography, materials, and sensing, as well as emerging areas of promise that will yield advances none of us can yet imagine.”

Governments also sense that there is a problem. For instance, the US Department of Energy, for instance, announced plans to provide $30M for quantum information science research on top of a 10-year $1.2B investment.

But why does quantum computing require PhD-level researchers whereas classic computer programming can be done by those with much lighter educational credentials?

One reason is that the concepts and tools of quantum computing are harder to grasp. The qubit is more complicated than the classical bit. Entanglement is not particularly intuitive. Unitary and hermitian matrices are not taught in grade school, and Shor’s algorithm is much more complex than Quicksort.

The other reason is that the programming environments for quantum computing are in their infancy. Teams are forced to work at the gate level or tweak existing building blocks. It is exceptionally hard to design and code new algorithms using existing tools. This makes the knowledge of quantum software scientists particularly relevant to the current development processes.

If you are a quantum information scientist, this is good news. With limited supply and rising demand, your services will become more expensive. For companies, the news is not so good. Beyond the obvious reason — it will cost more — companies that want to solve real-world problems such as finance, drug discovery or supply chain, will want to integrate domain-specific experts. But if a supply-chain expert needs to also be a quantum scientist…that is almost as rare as a magnetic monopole.

What to do? At Classiq, we believe we are helping to solve this issue by providing a platform that goes beyond gate-level programming. By expressing quantum algorithms in a high-level language, and having the Classiq code translate them into optimized quantum circuits, we allow companies to integrate domain-specific experts into their workflows and reduce the dependency on PhD-level scientists. As quantum computers become increasingly powerful, we believe that gate-level coding is not a scalable solution to solving complex real-life problems and that the Classiq platform can help do exactly that.

It was not too long ago that if you earned a Ph.D. in quantum physics, your job prospects would be quite limited. Now, there is an acute shortage of quantum physicists and quantum information scientists as companies race to form quantum computing teams.

Such is the high interest and strong demand that Harvard University recently launched a Ph.D. program in quantum science and engineering. Harvard president Larry Bacow notes that “[Harvard’s] faculty and students are driving progress that will reshape our world through quantum computing, networking, cryptography, materials, and sensing, as well as emerging areas of promise that will yield advances none of us can yet imagine.”

Governments also sense that there is a problem. For instance, the US Department of Energy, for instance, announced plans to provide $30M for quantum information science research on top of a 10-year $1.2B investment.

But why does quantum computing require PhD-level researchers whereas classic computer programming can be done by those with much lighter educational credentials?

One reason is that the concepts and tools of quantum computing are harder to grasp. The qubit is more complicated than the classical bit. Entanglement is not particularly intuitive. Unitary and hermitian matrices are not taught in grade school, and Shor’s algorithm is much more complex than Quicksort.

The other reason is that the programming environments for quantum computing are in their infancy. Teams are forced to work at the gate level or tweak existing building blocks. It is exceptionally hard to design and code new algorithms using existing tools. This makes the knowledge of quantum software scientists particularly relevant to the current development processes.

If you are a quantum information scientist, this is good news. With limited supply and rising demand, your services will become more expensive. For companies, the news is not so good. Beyond the obvious reason — it will cost more — companies that want to solve real-world problems such as finance, drug discovery or supply chain, will want to integrate domain-specific experts. But if a supply-chain expert needs to also be a quantum scientist…that is almost as rare as a magnetic monopole.

What to do? At Classiq, we believe we are helping to solve this issue by providing a platform that goes beyond gate-level programming. By expressing quantum algorithms in a high-level language, and having the Classiq code translate them into optimized quantum circuits, we allow companies to integrate domain-specific experts into their workflows and reduce the dependency on PhD-level scientists. As quantum computers become increasingly powerful, we believe that gate-level coding is not a scalable solution to solving complex real-life problems and that the Classiq platform can help do exactly that.

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