I am sure the quantum hype has reached every person in tech (and outside it, most probably). With some over-the-top claims, like “some company has proved quantum supremacy,” “the quantum revolution is here,” or my favorite, “quantum computers are here, and it will make classical computers obsolete.” I am going to be honest with you; most of these claims are intended as a marketing exaggeration, but I am entirely certain that many people believe that they are true.
The issue here is not whether or not these claims are accurate, but, as ML and AI professionals who need to keep up with what’s happening in the tech field, should you, if at all, care about quantum computing?
Because I am an engineer first before a quantum computing researcher, I thought to write this article to give everyone in data science an estimate of how much they should really care about quantum computing.
Now, I understand that some ML and AI professionals are quantum enthusiasts and would like to learn more about quantum, regardless of whether or not they will use it in their daily job roles. At the same time, others are just curious about the field and want to be able to distinguish the actual progress from the hype. My intention in writing this article is to give a somewhat lengthy answer to two questions: Should data scientists care about quantum? And how much should you care?
Before I answer, I should emphasize that 2025 is the year of quantum information science, and so there will be a lot of hype everywhere; it is the best time to take a second as a person in tech or a tech enthusiast, to know some basics about the field so you can definitively know when something is pure hype or if it has hints of facts.
Now that we set the pace, let’s jump into the first question: Should data scientists care about quantum computing?
Here is the short answer, “a little”. The answer is that, although the current state of quantum computers is not optimal for building real-life applications, there is no minimal overlap between quantum computing and data science.
That is, data science can aid in advancing quantum technology faster, and once we have better quantum computers, they will help make various data science applications more efficient.
Read more: The State of Quantum Computing: Where Are We Today?
The Intersection of Quantum Computing and Data Science
First, let’s discuss how data science, namely AI, helps advance quantum computing, and then we will talk about how quantum computing can enhance data science workflows.
How can AI help advance quantum computing?
AI can help quantum computing in several ways, from hardware to optimization, algorithm development, and error mitigation.
On the hardware side, AI can help in:
- Optimizing circuits by minimizing gate counts, choosing efficient decompositions, and mapping circuits to hardware-specific constraints.
- Optimizing control pulses to improve gate fidelity on real quantum processors.
- Analyzing experimental data on qubit calibration to reduce noise and improve performance.
Beyond the hardware, AI can help improve quantum algorithm design and implementation and aid in error correction and mitigation, for example:
- We can use AI to interpret results from quantum computations and design better feature maps for quantum Machine Learning (QML), which I will address in a future article.
- AI can analyze quantum system noise and predict which errors are most likely to occur.
- We can also use different AI algorithms to adapt quantum circuits to noisy processors by selecting the best qubit layouts and error mitigation techniques.
Also, one of the most interesting applications that includes three advanced technologies is using AI on HPC (high-performance computing, or supercomputers, in short) to optimize and simulate quantum algorithms and circuits efficiently.
How can quantum optimize data science workflows?
Okay, now that we have addressed some of the ways that AI can help take quantum technology to the next level, we can now address how quantum can help optimize data science workflows.
Before we dive in, let me remind you that quantum computers are (or will be) very good at optimization problems. Based on that, we can say that some areas where quantum will help are:
- Solving complex optimization tasks faster, like supply chain problems.
- Quantum Computing has the potential to process and analyze massive datasets exponentially faster (once we reach better quantum computers with lower error rates).
- Quantum Machine Learning (QML) algorithms will lead to faster training and improved models. Examples of QML algorithms that are currently being developed and tested are:
- Quantum support vector machines (QSVMs).
- Quantum neural networks (QNNs).
- Quantum principal component analysis (QPCA).
We already know that quantum computers are different because of how they work. They will help classical computers by addressing the challenges of scaling algorithms to process large datasets faster. Address some NP-hard problems and bottlenecks in training deep learning models.
Okay, first, thank you for making it this far with me in this article; you might be thinking now, “All of that is nice and cool, but you still haven’t answered why should I *a data scientist* care about quantum?”
You are right; to answer this, let me put my marketing hat on!
The way I describe quantum computing now is machine learning and AI algorithms from the 1970s and 1980s. We had ML and AI algorithms but not the hardware needed to utilize them fully!
Read more: Qubits Explained: Everything You Need to Know
Being an early contributor to new Technology means you get to be one of the people who help shape the future of the field. Today, the quantum field needs more quantum-aware data scientists in finance, healthcare, and tech industries to help move the field forward. So far, physicists and mathematicians have controlled the field, but we can’t move forward without engineers and data scientists now.
The interesting part is that advancing the field from this point doesn’t always mean you need to have all the knowledge and understanding of quantum physics and mechanics, but rather how to use what you already know (aka ML and AI) to move the technology further.
Final thoughts
One of the critical steps of any new technology is what I like to think of as the “last hurdle before the breakthrough.” All new technologies faced pushback or hurdles before they proved helpful, and their use exploded. It is often difficult to pinpoint that last hurdle, and as a person in tech, I am fully aware of how many new things keep popping up daily. It is humanly impossible to keep up with all new advances in technology in all fields! That is a full-time job by itself.
That being said, it is always an advantage to be ahead of the demand when it comes to new technology. As in, be in a field before it becomes “cool.” By no means am I telling data scientists to quit their field and jump on the quantum hype train, but I hope this article helps you decide how much or little involvement you, as an ML or AI professional, would want to have with quantum computing.
So, should ML and AI professionals care about quantum? Only enough to be able to decide how it can affect/ help with their career progress.