Quantum Machine Learning: Is It Engineering Or Research?
The Tension Between Working Code And Deeper Meaning Of Learning
Quantum Machine Learning promises results that seem otherwise impossible. But what does that really mean for you? Is it about building pipelines that work today? Or is it about asking the questions that shape tomorrow? The answer may decide not just how you work, but where you'll stand when the field breaks through.
by Frank ZickertAugust 26, 2025
Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches.
That sounds very much like a technical challenge.
Figure 1. Quantum Machine Learning as an engineering process
Consider what Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. involves: Integrating a A quantum algorithm is a step-by-step computational procedure designed to run on a quantum computer, exploiting quantum phenomena such as superposition, entanglement, and interference to solve certain problems more efficiently than classical algorithms. into a A machine learning pipeline is a structured sequence of steps that takes raw data through preprocessing, model training, evaluation, and deployment to produce actionable outputs in a repeatable way., operating them reliably, and increasing their performance. You build the system, measure its speed, and compare it with classical methods. You combine two powerful technologies to meet a clear goal.
Surprisingly, the true meaning of these words fades the longer you work with Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. . What does combine really mean? And what actually counts as otherwise impossible? Is it just about building pipelines that run on fragile Quantum Hardware, or does it also mean exploring deeper questions, such as how the nature of learning changes when the data itself is quantum-based?
The real question is not simply what Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is, but what it means to work with it.
When you step into this field,
are you taking on the role of an engineer? Are you building systems that run on Noisy Intermediate-Scale Quantum refers to the current generation of quantum devices that have enough qubits to run non-trivial algorithms but are still small and error-prone, limiting their reliability and scalability.Quantum Hardware?
Or are you acting as a researcher? Do you ask what it means to learn in a quantum world and pushing toward principles that outlast any single prototype?
How you answer this question is important. It determines not only how you describe yourself, but also how you structure your work and which problems you tackle. Moreover, it determines the extent to which you enable yourself to benefit from the field as soon as it begins to deliver on its promises. Or whether you might even help shape the field.
It's about nothing less than the question: What's in it for you?
When you first start working with Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. , you don't begin with elegant theories or sophisticated frameworks. You dive straight into the chaos of systems that don't behave the way you want them to. You feel the hum of Quantum Hardware that is Quantum noise and far too small to do what you want it to do. You struggle with APIs that feel like half-baked instructions and juggle scarce A qubit is the basic unit of quantum information, representing a superposition of 0 and 1 states.s as if they were fragile glass marbles that could shatter at the slightest misstep. And then there's Debugging is.... Endless, exhausting, nerve-wracking Debugging is....
Have You Seen The Devilish Qiskit Squirrel Sabotaging You
The Struggle for Learning in Qiskit’s New Era
There is something lurking in the dense thicket of learning Qiskit. You don't see it at first. It hides behind method calls, peeks out between class definitions, and scurries away as soon as you're sure you've found the right parameter format.
Figure 2. How progress in quantum machine learning feels like
Strangely enough, however, it is precisely this part that keeps you going. Because despite the frustration, there is a certain satisfaction: the work is concrete, manageable, visible. You can watch your progress take shape in prototypes that finally work after nights of trial and error. It's like getting a wild animal to take food from your hand. The moment it works, however clumsily, you feel the spark of possibility.
In this mode, progress is measured by the first time your code does not crash, by the curve in the graph that finally shows a line where there was previously only noise, by the sudden elation of robustness where there was previously only fragility. As a Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. engineer, this becomes your lifeline: results you can point to, algorithms that hold together, systems that finally hum instead of squeak.
You build, you test, you iterate. And when you deliver something that didn't exist yesterday, you feel the relief of having subjugated an impossible machine to your will. In a field as young as Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. , this competence feels indispensable. It protects you from the fear that all of this could be nothing more than speculation.
But just as you start to breathe again, just as you think you've found your rhythm, the more difficult questions creep in. The ones that no benchmark alone can answer.
You can wire Hybrid quantum-classical is... models, test Kernel is...s, and get A machine learning pipeline is a structured sequence of steps that takes raw data through preprocessing, model training, evaluation, and deployment to produce actionable outputs in a repeatable way.s up and running. But very quickly, you encounter questions that defy an engineering mindset.
Why does a particular Quantum Feature Map is... seem to help with one dataset but fail with another? What does it even mean for a model to learn when its input space consists of Quantum State is...s instead of classical data? Are we exploring real computational advantages, or are we just exploiting the peculiarities of today's small devices?
These are not issues that can be fixed with a patch. These are things that point to something deeper: Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is more than engineering.
Figure 3. When the linearity of an engineering process disappears
At this point, the linearity of an engineering process disappears. One begins to realize that Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is not just about making things work, but also about uncovering principles that explain why they work, when they fail, and what they could become.
Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches.
When putting the emphasis on the exploration of how then Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. starts sounding very much like a research field.
Suddenly, Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is about discovering new ways to represent Quantum Information, asking what learning means when the data itself is quantum-based, and searching for principles that can be generalized.
So which is it?
The real danger lies not in deciding whether Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is engineering or research. It lies in reducing Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. to just one of these disciplines, as if one were sufficient. This creates blind spots and obscures what Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. really demands.
If you view Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. purely as engineering, integration challenges appear to be the frontier of knowledge. You may successfully develop systems that work today, but you run the risk of overlooking the deeper principles that become important once the Quantum Hardwarematures. In doing so, you trivialize the open questions whose answers are essential for Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. to deliver on its promises.
Figure 4. Quantum machone learning is about uncovering principles
If you view Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. purely as research, you run the risk of distancing yourself from the messy realities of ambiguous data, noisy devices, and algorithms that actually have to solve problems. You can write essays about expressiveness or advantages, but without systems that work in practice, these claims remain untested and therefor speculative.
But perhaps it is precisely this tension that defines the character of Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. .
So, what if Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is not either engineering or research? What if Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is the combination of engineering and research? A dual practice that creates artifacts while expanding understanding.
Engineering in Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. means solving problems under certain constraints. You take today's Quantum Hardware and frameworks as they are: noisy A qubit is the basic unit of quantum information, representing a superposition of 0 and 1 states.s, limited depth, integration problems. Your success is measured by working systems: A machine learning pipeline is a structured sequence of steps that takes raw data through preprocessing, model training, evaluation, and deployment to produce actionable outputs in a repeatable way. that run, benchmarks that improve, demos that prove feasibility.
Research in the field of Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. means asking questions that go beyond a single system. What formalizes a Quantum Feature Map is...? How should we measure expressiveness in Hybrid quantum-classical is... models? Can we characterize when quantum models offer real advantages over classical models? Success here is measured not only by a prototype, but by knowledge that can be generalized, such as concepts, principles, and methods that others can build on.
Fortunately, we don't need to develop a new approach for such a combination of engineering and research. We can use the Hevner, A., 2004, MIS Quarterly, Vol. 28, No. 1, pp. 75-105. Design Science Research is... offers a way to combine two seemingly separate ways of working. The premise is simple: knowledge is created through the creation and evaluation of artifacts in their domain.
Figure 5. Three-Cycle View of Design Science Research according to Hevner 2007
On the left, the Environment provides the application domain(people, organizational and technical systems) and defines problems and opportunities. These drive the Relevance Cycle, which supplies requirements to design activities and enables field testing of solutions.
On the right, the Knowledge Base contributes scientific theories, methods, experience, and expertise. The Rigor Cycle grounds the research in established knowledge and ensures contributions back into the body of knowledge through new artifacts and processes.
At the center, the Design Cycle focuses on building and evaluating design artifacts and processes. This iterative loop connects to both relevance and rigor, ensuring that designs are both practically useful and scientifically sound.
In short, the framework emphasizes a balance: solutions must address real-world needs (relevance) while also extending or grounding themselves in scientific knowledge (rigor), with the design cycle as the operational core.
Design Science Research is... has adopted the engineering focus on artifacts, i.e., systems, methods, or prototypes developed to solve real problems. This is the application domain side: creating something useful under certain constraints.
In Design Science Research is..., these are not separate from each other. Creating an artifact and evaluating it is the research process itself. Ordinary problem solving deals with a situation; Design Science Research is... deals with a class of problems, making the solution reusable and the findings transferable.
Figure 6. Quantum machone learning is both: engineered and a research contribution
From this perspective, Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. projects do not need to be divided between engineering and research. For example, a prototype quantum-classical machine learning pipeline designed and evaluated in response to a general class of learning problems is both:
An engineered artifact that solves a specific problem at hand.
A research contribution, as it broadens our understanding of the types of problems such pipelines can solve.
So, if work in the field of Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. combines the practical creation with the scientific evaluation of artifacts, what does that mean for your positioning?
What It Takes To Become A Quantum Machine Learning Researcher
Things you need to learn... and things you don't.
You don't need to master everything inside quantum computing and machine learning. But there are a few things you'd better know.
Firstly, this means that hybrid roles are the norm rather than the exception. Many Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. engineers publish research papers when their prototypes yield general insights. Many Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. researchers ultimately write production-ready code, as this is the only way to test their hypotheses. The boundary is deliberately designed to be permeable.
Secondly, it offers a wide range of career planning options. If you are interested in engineering, you will be valued for developing systems that make Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. tangible today. Your main focus will be pipelines, libraries, and integrations. If you are interested in research, you will be valued for developing principles that influence the future development of others. If you can do both, you will find yourself at the intersection of breakthroughs. You'll work on artifacts that prove feasibility and theories that explain why they matter.
Finally, it is a matter of long-term strategy. Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is still in its infancy. The decisive advances may come from related fields (Quantum Hardware architectures, algorithmic innovations, Quantum Error Correction). If you are able to bridge the gap between engineering and research, you will be in pole position when these breakthroughs come, and you can either translate them into systems or theories. Or both.
Figure 7. Engineering and research work hand in hand
Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. is about achieving results. Yet, the meaning of these words has changed. The combination of Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. and Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. is no longer just about linking toolkits or embedding a Quantum Circuit into a familiar A machine learning pipeline is a structured sequence of steps that takes raw data through preprocessing, model training, evaluation, and deployment to produce actionable outputs in a repeatable way.. The combination is both methodological and technical: engineering and research work hand in hand, each shaping and constraining the other. A prototype forces clarity about what can be executed on current devices, while analysis and theory reveal why this artifact is significant and what it implies beyond its immediate use.
Similarly, the wording otherwise impossible is no longer a shorthand for faster runtimes or pure computational advantages. It begins to describe a more comprehensive impossibility: the limits of what we can understand, explain, and design when we cling to only one dimension of work. Without engineering, theories never encounter the resistance of real Quantum Hardware and remain speculative. Without research, artifacts risk being local hacks with no lasting value. In the tension between these two dimensions, something truly new becomes possible.
That is what working in the field of Quantum Machine Learning is the field of research that combines principles from Quantum Computing is a different kind of computation that builds upon the phenomena of Quantum Mechanics. with traditional Machine Learning is an approach on solving problems by deriving the rules from data instead of explicitly programming. to solve complex problems more efficiently than classical approaches. ultimately means: entering a space where the artifact is never just a tool and the theory is never just an abstraction. One nourishes the other, and together they push the boundaries of what is considered possible.
