Do I Need A Ph.D. To Land A Job In Quantum Machine Learning?

My Two Quanta On Quantum Computing's Next Challenge

This post is available on Medium, too.

Last week, I had two interesting conversations with fellow quantum machine learning students. Interestingly, they shared one major concern: “Will my efforts in learning quantum machine learning be recognized? Do I need a formal certification or even a Ph.D. to land a job in quantum machine learning?”

Of course, it would be presumptuous to claim I knew what exact formal qualification is required to land a job in quantum machine learning. After all, I am not a recruiter in the field.

Therefore, all I can give is my two quanta.

The expression “my two cents” is shorthand for “my two pennies worth.” It is a way of offering one’s opinion and saying that it is only worth two pennies. While I like this humble way of valuing an opinion at only two cents, it is inappropriate in the context of quantum computing. For a quantum (plural quanta) is the smallest possible discrete unit of any physical property, I believe “my two quanta” is a better measure of my opinion.

Coincidentally, I recently read an article that sheds light on quantum computing companies' struggles to find appropriate candidates and why they are looking for Ph.D.s. Understanding what employers are really looking for is your advantage in landing a job.

Note: passages formatted as citations are directly or indirectly taken from that article.

“Employers need quantum employees with interdisciplinary skills: on top of a background in quantum physics, some sort of experience with data analysis, engineering, modeling, or programming, among other things, will also be a must-have. That level of specialisation isn’t common; in fact, it mostly exists at the PhD level.”

If we read this quote attentively, it clears up a widespread misconception. (Good) employers do not look for a formal qualification for the sake of it. They need someone who’s up to the job to be done. Formal qualification is a means because they found it to correlate with good candidates. What they really need is candidates specialized in a specific set of interdisciplinary skills.

Bachelor's and Master's programs usually focus on a certain discipline, such as physics, math, or computer science. But we do not yet have those programs that teach the required mix. But as a Ph.D., you usually define your own specific scope. And, it is the scope you set for yourself that employers are looking for. It is not that one piece of paper with your name on it.

So, the important question is not whether you have a Ph.D. Instead, the question is whether you can demonstrate this specialization in the right mix of skills.

“Finding someone with the right skill mix is the biggest challenge,” Ross Duncan, the head of quantum software at Cambridge Quantum, tells ZDNet. “It’s happened only a handful of times among the people that we hired to get someone who was ready to start when they walked in the door.”

As someone interested in entering the field, the best you can do is to develop these skills. So, the first step is to become clear about the skills you need. Then, do a self-assessment. Which skills do you already have, and which skills are you short of? Finally, you need to fill the gap.

As simple as this sounds in theory, it can be pretty tricky in practice. And, I don’t even refer to the actual learning. The real tricky part is how to demonstrate that you filled the gap. Again, it circles back to formal education. Nowadays, not only universities provide diplomas. Almost any online course offers some certification.

But what if you prefer reading a good old book? Maybe, you even read a whole bunch of books. You may have invested a lot of time into learning. But you won’t get a diploma or a certificate. So does it mean all your efforts are worthless for landing a job?

I can completely understand the longing for a formal certificate to prove your abilities. But if we think about it, I’d really doubt that formal certificates prove anything. I believe you can get a lot of certificates without really mastering the respective fields.

So, let’s look at it from the recruiter’s perspective again. The recruiter is looking for a person with extraordinary abilities in an interdisciplinary field. She knows about the specificity of her demand. There’s no blueprint for what she’s looking for. Consequently, she has to evaluate each candidate individually. Of course, she may reject candidates that have absolutely no education right away. But if you have a computer science degree from a university, I’d think you are qualified for further consideration.

Don’t get me wrong. By further consideration, I don’t mean you got the job already. I don’t even say she will invite you for an interview. But, I’d say she checks your CV to see what you have to offer.

Now, you need something to stand out. This is your chance to demonstrate your specialization in the right mix of skills. But I doubt a list of certificates will do.

Instead, I’d follow a simple rule: Show, don’t tell.

Preferably, you demonstrate that you did not only clicked through an online course, but you’re able to apply your knowledge.

It is good practice to have a portfolio of your work in other disciplines, such as software development or data science. It showcases the projects you have worked on. Desirably, these are real cases. But quantum machine learning is a new field. So, you should be fine showing how you applied your knowledge on open-source datasets and exemplary problems, too.

A portfolio is your chance to creditably demonstrate how you filled the gaps in your formal education. In addition, it offers a path to building trust with a recruiter — beyond formal certificates.

Keep in mind quantum machine learning is an emerging field. There is no typical qualification yet. This is not only a problem for candidates but also recruiters. As a substitute for the one and only qualification, recruiters ask for the next best thing to look for in a candidate — a Ph.D.

So, my recommendation is not to get intimidated by too specific formal demands. And, don’t get obsessed with certificates, either. Instead, make sure you master the demanded skills. Do this in any way that suits your personal way of learning. But, make sure to demonstrate what you’ve learned.

“Estimates predict the quantum computing industry will be a $65 billion market by 2030”

If that prediction is even close to the truth, the demand for skills will exceed the demand for formal titles.

“And with businesses set to be fighting over quantum-qualified job seekers in the near future, now might be the right time to start studying.”

---

You're interested in quantum computing and machine learning...

...But you don't know how to get started?