I’m sure the quantum hype has reached everyone with high tech (and perhaps outside of that). “Some companies have proven quantum advantage,” “The quantum revolution is here,” or my favorite, “Quantum Computers is here, and classic computers are going to be abolished.” , with some over-the-top claims, I’m going to be honest with you. While most of these claims are meant as marketing exaggerations, I am totally certain that many people believe they are true.
The question here isn’t whether these claims are accurate, but as ML and AI experts, it’s important to keep up with what’s going on in the tech space, if any. Should I care about quantum computing?
As I am the first engineer before a quantum computing researcher, I thought about writing this article to provide everyone in data science with an estimate of how much quantum computing should really care about.
Now I’m interested to learn more about quantum, whether some experts in ML and AI are quantum enthusiasts and use it in their daily work roles. At the same time, others are interested in the field and want to be able to distinguish actual progress from hype. My intention in writing this article is to provide a somewhat longer answer to two questions. Should data scientists be interested in quantum? And how much do you have to care?
Before we answer, we need to emphasize that 2025 is the year of quantum information science. That’s why there’s a lot of hype everywhere. As a technology and tech enthusiast, it’s the perfect time to learn the basics about the field.
Now that you have set your pace, let’s jump to the first question. Should data scientists be interested in quantum computing?
Here is the short answer, “a little.” The answer is that while the current state of quantum computers is not optimal for building real applications, there is no minimal overlap between quantum computing and data science.
In other words, data science will help quantum technology move faster, and once better quantum computers are created, it will help make various data science applications more efficient.
Read more: Quantum Computing State: Where are you today?
The intersection of quantum computing and data science
First, let’s explain how data science, or AI, can help advance quantum computing. Next, we’ll explain how quantum computing can enhance your data science workflow.
How can AI advance quantum computing?
AI can assist quantum computing in several ways, from hardware to optimization, algorithm development, and error mitigation.
On the hardware side, AI can help:
Optimize the circuit by minimizing the number of gates, efficient decomposition selection, and mapping the circuit to hardware-specific constraints. Optimize the control pulses to improve the fidelity of the actual quantum processor gates. Analyze experimental data on Kikubit calibration to reduce noise and improve performance.
Beyond hardware, AI can help improve the design and implementation of quantum algorithms, helping to help correct and mitigate errors, for example.
You can use AI to interpret quantum calculation results and design great feature maps for Quantum Machine Learning (QML). This will be explained in a future article. AI can analyze quantum system noise and predict which errors are most likely to occur. It also allows quantum circuitry to be adapted to noisy processors by using a variety of AI algorithms to select the optimal qubit layout and error mitigation techniques.
Also, one of the most interesting applications, including three advanced technologies, is to use AI on HPC (in short, high performance computing, or supercomputers) to efficiently optimize and simulate quantum algorithms and circuits. It’s about doing it.
How can Quantum optimize your data science workflow?
Now, as we’re working on some of the ways AI can help take Quantum Technology to the next level, we can now address the ways Quantum can help optimize your data science workflow.
Before you dive in, let me remind you that Quantum Computers has very good (or very good) optimization issues. Based on that, some areas where Quantum can help are:
Solve complex optimization tasks faster, like supply chain issues. Quantum computing has the potential to process and analyze large datasets. Quantum Machine Learning (QML) algorithms make training faster and improve models. An example of a QML algorithm currently being developed and tested is Quantum Support Vector Machines (QSVMS). Quantum Neural Network (QNNS). Quantum Principal Component Analysis (QPCA).
We already know that quantum computers are different depending on how Quantum Computers works. They help classic computers by addressing the challenges of scaling algorithms and processing large data sets faster. Addresses some NP hardware issues and bottlenecks in training deep learning models.
Well, first of all, thank you for making it with me in this article. You may be thinking now.
You’re right; to answer this, try wearing a marketing hat!
The methods to explain current quantum computing are machine learning and AI algorithms of the 1970s and 1980s. There were ML and AI algorithms, but there was no hardware needed to fully utilize them!
Read more: Qubits Description: Everything you need to know
Being an early contributor to new technology means being one of the people who help you shape the future of your field. Today, Quantum Field needs more quantum recognition data scientists in the financial, healthcare and tech industries to help them move forward in the field. So far, physicians and mathematicians have controlled this field, but they are currently unable to move forward without engineers and data scientists.
The interesting part is that moving the field from this point does not always require all knowledge and understanding of quantum physics and mechanics, but rather has a way of using things you already know (aka ml and ai). This means that it is. Even further away.
Final thoughts
One of the key steps in new technology is what I would like to consider as the “final hurdle before a breakthrough.” All new technologies faced pushbacks and hurdles before they proved useful, and their use exploded. Identifying that final hurdle is often difficult, and as a tech person, I am completely aware of how many new things keep popping up every day. It is impossible for humans to keep up with all the new advances in technology in all areas! It’s a full-time job in itself.
That being said, when it comes to new technologies, being ahead of demand is always an advantage. Similarly, he’s on the field before he’s “cool.” I will never tell a data scientist to quit his field and jump on a quantum hype train, but as an expert in ML or AI, this article will be about, or almost, how involved or involved in quantum computing. I hope it helps you decide whether or not.
So, should ML and AI experts care about quantum? It is enough to determine how it can impact/help their career progression.
