Quantum computing and the IT gap

• Quantum computing is expected to become a functioning reality in the next seven years.
• The IT sector already has a skills gap.
• Quantum computing is likely to add new skills to the shortage.

Quantum computing is expected to become a functioning reality within a generation, with many leading companies predicting it will be an adoptable technology by 2030. That’s going to bring a significant difference to traditional IT teams, as quantum computing is likely to involve different problems, different solutions, and a fairly new methodology to what we think of as the IT team’s role.

The question is, given that there’s a pre-existing IT skills gap, will companies be ready to make the most of early adoption of quantum computing as and when it arrives? And if not, how can they get ready for it, technically before the “finished product” of a fully operating fault-tolerant quantum computer is available to test?

We sat down with Scott Bucholz, Global Quantum Lead at Deloitte Consulting, to crunch the numbers on quantum computing and the IT gap.

Setting a date.

THQ:

First thing’s first – when are we really expecting quantum computing to be a thing? IBM says 2030, but there are forecasts that put it ahead of that. So – roughly how long do you think we have before quantum computing is a reality that we – and our IT teams – have to face?

SB:

I go through periods of optimism and periods of pessimism on that question.

In an optimistic mood, I think what we’re likely to see in the next couple of years are small things, improving the situation in corners. Sometimes, explaining to somebody that there’s a small “in the corner” problem, that’s still interesting and adds value, is harder than being able to say everything everywhere is better.

That’s the challenge around dating “the change.” People seem to think quantum computing will arrive all at once, making everything better everywhere, in one thunderbolt moment. I’m cautiously optimistic that there are things that will be done with quantum computing in the next couple of years that will have real value to businesses. Now, whether or not those look like the things people most care about is a different question. But I think they will have value.

THQ:

What sort of things are you talking about?

SB:

My suspicion, my best suspicion at the moment is that there will probably be things in areas of machine learning. And given the limitations of the technology, machine learning or optimization will probably be deployed in areas where quality is more important than time.

So I don’t suspect there will be real-time things. But I do suspect there will be areas where quality of results and prediction, or quality of optimization, trumps the need to have it immediately.

But then, the way things are going, come back to me in three months and we’ll see if the things I’m cautiously optimistic about at the moment are borne out.

“It’s aliiiive!”

THQ:

A lot of people are expecting the lightning bolt moment on quantum computing – the Frankenstein moment, where someone somewhere pulls a lever and all of a sudden “It’s aliiiiive!” Whereas it seems to be more of a sort of a gradual growth. Bits and pieces of cool stuff that work in different ways, or that weren’t possible before but now work because of new ways of doing things.

SB:

The challenge that people fail to appreciate sometimes is that we can reasonably predict engineering, right? Not entirely, but you can have an opinion, an informed opinion, about what an engineering timeline looks like.

There are still research problems that need to be solved, and research breakthroughs are not as predictable. And my suspicion is that people are looking at the range of problems, and they’re saying, “I believe, implicitly, there’s some research thing that’s going to happen. And then we’ll all have the giant Frankenstein switch moment. And it’ll be really exciting.”

 

THQ:

So how do we think the creeping evolution of quantum computing will change the makeup of IT departments in the next few years? What are the extra skill sets that IT professionals will need if they’re to make best use of what’s coming?

SB:

If you look at the history of most technologies, they tend to start out with very specialized skill sets at the beginning – so, for instance, we’ll need quantum physicists to understand deeply what’s going on under the covers of quantum computing.

What tends to happen over time is that the degree of specialization required goes down. As time goes on, and there are only so many quantum physicists in the world, for instance, it moves from a specialization phase to an operational phase. So we’ll see a movement from quantum physicists, where everybody needs a PhD, to more macro-practical skills – people are already talking about quantum information science. That may not require master’s degrees or bachelor’s degrees.

Clearly, there will be a whole ecosystem of people, and we need to support things. I suspect, though, that from an IT department perspective, what we’re going to find is that for the foreseeable future, at least the next five years, we need people skilled in understanding the limitations of the hardware itself.

The training lag.

We’ve had “classical” computing for 60 years. Which means we’ve had 60 years to improve it and refine it, to the point where the number of people and the nature of what it takes to do useful work is small, because we’ve evolved it to the point where it’s not that hard.

What we’ve found is that it typically takes us a year or two to retrain somebody from where they are today to being productive using quantum computers. And even that depends on the hardware, it depends on that person’s proclivity for physics and math in some cases. What I would say is that in general, I think in a decade, we’re going to see the level of abstraction go way up, and the need to understand deeply go way down.

But in the meantime, it’s a lot like watching the evolution of data science. If you remember the evolution of data science, it started with a bunch of physicists, right? And now it’s moved down to people with master’s degrees.

But even at the beginning, what people found is that you could take people who were less skilled, but you had to pair them with somebody who really knew what they were doing to bring them along on the journey.

That’s what organizations are likely to find with quantum computing, too. It’s going to be a similar journey. You start by hiring the PhDs in quantum information science, and then you cross-train the rest of the team in terms of what’s going on, so that we can all go up the power curve together.

 

In Part 2 of this article, we’ll face the fact that where we are in relation to quantum computing staffing and where we need to be, are in no sense the same place – and work out ways to bridge the gap.