NEWSLETTER
I am sure that quantum exaggeration has reached all people in technology (and outside it, most likely). With some exaggerated statements, such as “some company has demonstrated quantum supremacy”, “the quantum revolution is here” or my favorite, “quantum computers are here, and will make obsolete classic computers.” I will be honest with you; Most of these statements are intended as an exaggeration of marketing, but I am completely sure that many people believe they are true.
The problem here is not whether or not these statements are precise, but, as ML and ai professionals who need to keep up with what is happening in the technological field, should, if, if it cares ?
Because I am an engineer first before a quantum computing researcher, I thought about writing this article to give everyone in data science an estimate of how much they 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 work roles. At the same time, others simply are curious about the field and want to distinguish the real progress of exaggeration. My intention when writing this article is to give a somewhat long answer to two questions: Should data scientists worry about Quantum? And how much should you import?
Before answering, I must emphasize that 2025 is the year of quantum information science, so there will be a lot of advertising everywhere; It is the best time to take a second as a person in technology or technology enthusiast, to know some basic concepts about the field so that you can definitely know when something is a pure hype or if you have touches of facts.
Now that we establish the rhythm, let's jump to the first question: Should data scientists worry about quantum computing?
Here is the short answer, “a bit”. The answer is that, although the current state of quantum computers is not optimal to build real -life applications, there is no minimal overlap between quantum computing and data science.
That is, data science can help advance quantum technology faster, and once we have better quantum computers, they will help make several 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, we discuss how data science, namely ai, helps to advance quantum computing, and then we will talk about how quantum computing can improve the work science flows.
How can ai help advance quantum computing?
ai can help quantum computing in several ways, from hardware to optimization, algorithms development and error mitigation.
On the hardware side, ai can help in:
- Optimization of circuits minimizing gate counts, choosing efficient decompositions and mapping circuits to specific hardware restrictions.
- Optimization of control pulses to improve the loyalty of the door in real quantum processors.
- Experimental data analysis on Qubit calibration to reduce noise and improve performance.
Beyond hardware, ai can help improve the design and implementation of the quantum algorithm and help in the correction and mitigation of errors, for example:
- We can use ai to interpret the results of quantum calculations and design better feature maps for automatic quantum (QML), which I will address in a future article.
- ai can analyze the noise of the quantum system and predict which errors are most likely to occur.
- We can also use different ai algorithms to adapt the quantum circuits to noisy processors by selecting the best QBIT designs and error mitigation techniques.
In addition, one of the most interesting applications that includes three advanced technologies is the use of ai in HPC (high performance or supercomputer computing, in summary) to optimize and simulate quantum algorithms and circuits efficiently.
How can Quantum optimize the workflows of data science?
Agree, now that we have addressed some of the ways in which ai can help bring quantum technology to the next level, we can now address how Quantum can help optimize the work science flows.
Before immersing ourselves, let me remind you that quantum computers are (or will be) very good in optimization problems. According to that, we can say that some areas where the amount it will help is:
- Solve complex optimization tasks, such as supply chain problems.
- Quantum computing has the potential to process and analyze exponentially faster massive data sets (once we reach better quantum computers with lower error rates).
- The Quantum Machine Learning (QML) algorithms will lead to faster training and improved models. Examples of QML algorithms are currently being developed and tested are:
- Quantum support vector machines (QSVMS).
- Quantum neural networks (qnns).
- Analysis of main quantum components (QPCA).
We already know that quantum computers are different due to how they work. They will help classic computers addressing the challenges of climbing algorithms to process large faster data sets. Address some problems and bottlenecks NP-NP in the training of deep learning models.
Well, first, thanks for getting so far with me in this article; You could be thinking now “All of that is pleasant and great, but you have not yet answered why should * a data scientist *?
You're right; To answer this, let me put on my marketing hat!
The way in which I describe quantum computing is now automatic learning and algorithms of the seventies and eighties. We had ML and ai algorithms, but not the hardware necessary to completely use them.
Read more: Qubits explained: everything you need to know
Being an early taxpayer to the new technology means that it can be one of the people who help shape the future of the field. Today, the quantum field needs more data scientists as many in the finance, medical care and technology industries to help advance in the field. Until now, physicists and mathematicians have controlled the field, but we cannot advance without engineers and data scientists now.
The interesting part is that advancing in the field from this point does not always mean that you need to have all the knowledge and understanding of physics and quantum mechanics, but how to use what you already know (also known as 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 like the “last obstacle before progress.” All new technologies faced recoil or obstacles before they proved useful, and their use exploded. It is often difficult to identify that last obstacle, and as a person in technology, I am fully aware of how many new things continue to appear daily. It is humanly impossible to stay up to all the new advances in technology in all fields! That is a full -time job for itself.
That said, it is always an advantage to be ahead of demand when it comes to a new technology. As in, being in a field before it becomes “great.” In no way I am telling data scientists to renounce their field and go out to the quantum hype train, but I hope this article helps him decide how much or little participation, as a ML or ai professional, I would like to have with computing quantum
So should ML professionals worry about Quantum? Just enough to decide how you can affect/ help with the progress of your career.
