The term quantum advantage is used a lot — and often incorrectly. In its strict sense, quantum advantage means: a quantum approach solves a well-defined task better than the best known classical approach, under fair comparison. That’s a very high bar. A few important clarifications: - Quantum advantage is task-specific, not general - It does not mean “quantum is faster at everything” - It does not mean replacing classical ML pipelines - It often depends on how the problem is formulated In many cases, what looks like “advantage” disappears once: - classical baselines are improved - hybrid methods are compared fairly - overheads are accounted for This is why most serious researchers and practitioners are cautious with the term. Today, much of the real value of quantum work is in: - understanding new representations of problems - exploring alternative modeling assumptions - identifying where advantage might eventually appear Not in claiming performance wins prematurely. In business and applied settings, this distinction matters a lot — because investing based on vague notions of “quantum advantage” is risky. In this community, we’ll always be careful with this language, and we’ll separate: - theoretical possibility - experimental demonstrations - and practical usefulness They are not the same thing. Question: When you hear “quantum advantage,” what do you usually think it means?

One thing I want to be very clear about — and this is based on how things are actually done today — is this: Quantum computers do not work in isolation. Every serious quantum workflow today is: - driven by classical computation - controlled by classical optimization or ML - evaluated with classical metrics The quantum part, when used, is a small component inside a much larger classical pipeline. For example: - Data preprocessing is classical - Model selection is classical - Training loops are classical - Decision-making is classical The quantum system, if used at all, acts as: - a feature generator - a sampler - a structured model component - or an experimental subroutine This is why framing quantum as a “replacement” for classical computing is misleading. A more accurate way to think about it is: Quantum is a new modeling ingredient — not a standalone solution. This hybrid view is not a compromise. It’s simply how current hardware, algorithms, and theory actually work. In this community, we’ll always discuss quantum in this hybrid, realistic context, because that’s the only way it connects to real ML systems and real business workflows. Question: When you think about quantum, do you imagine it replacing something — or augmenting something that already exists?

When someone asks me about using quantum computing, I don’t start with qubits, circuits, or hardware. I usually start with a few very simple questions. 1. What is the actual problem? Not the tech description — the real business or research goal. 2. What is currently not working well?Is it accuracy, cost, time, scalability, or something else? 3. What classical or ML methods have already been tried?Often there are simpler improvements still available. 4. Where is the bottleneck?Data, modeling assumptions, optimization, or computation? 5. What would success look like?Even a small improvement can be valuable — but it has to be clearly defined. Only after this do we even mention quantum. In many cases, the honest answer is: quantum is not needed here — at least not right now. And that’s a good outcome. This way of thinking avoids hype-driven decisions and helps teams invest their time and money wisely. In this community, I’ll keep sharing how I think about these trade-offs — because good judgment matters more than fancy technology. Question: If you’re exploring quantum, which part feels most unclear right now — the problem, the tools, or the expectations?

When people first get interested in quantum computing, the question I hear most often is: “How can I use a quantum computer?” That’s almost always the wrong place to start. In practice, the better questions are: - What problem are we actually trying to solve? - Why do current classical or ML approaches struggle? - Is the bottleneck data, computation, or modeling? - What would “better” even mean in this context? Only after these are clear does it make sense to ask whether quantum plays any role at all. This is why most successful work today doesn’t begin with hardware or algorithms. It begins with problem formulation. In many cases, the conclusion is: “Quantum is not needed here.” And that’s a good outcome — it saves time, money, and effort. In this community, we’ll approach quantum from this angle: clear thinking first, technology second. If you’re exploring quantum for a real problem, this mindset matters far more than knowing how qubits work. Question for you: What kind of problem are you currently thinking about — research, business, optimization, ML, or something else?

Short answer: not much — at least not on their own. Today’s quantum computers are: - noisy - small - expensive to run - and not ready to replace classical systems If someone tells you otherwise, they’re overselling. So why are researchers and companies still interested? Because quantum computers: - let us experiment with new ways of modeling problems - can act as specialized components inside larger classical workflows - help us understand where future advantages might emerge In practice, almost all meaningful work today is: - hybrid (classical + quantum) - exploratory - and problem-specific This is why the right starting question is never: “How do I use a quantum computer?” It’s: “What problem am I trying to solve, and why do classical tools struggle here?” In this community, we’ll be very clear about this: quantum computing today is about learning, structure, and preparation — not immediate performance wins. If you’re here for honest discussions, you’re exactly where you should be. Question: What have you heard about quantum computers that you’re most skeptical about?