🧪 How I Usually Evaluate Whether Quantum Is Worth Considering

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.

What is the actual problem? Not the tech description — the real business or research goal.
What is currently not working well?Is it accuracy, cost, time, scalability, or something else?
What classical or ML methods have already been tried?Often there are simpler improvements still available.
Where is the bottleneck?Data, modeling assumptions, optimization, or computation?
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?

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