My approach to learning quantum computing always begins with the physics. If the intuition behind the model is weak, the code usually becomes mechanical and difficult to extend in a meaningful way.
After that, I move into implementation. Small experiments in Qiskit, Julia, or Python help me test whether I actually understand the concept instead of only recognizing the terminology.
The most useful habit for me is repetition through projects. A concept becomes clearer when it is applied in different forms, whether that means simulation, optimization, or machine learning workflows.
Start from the physics
The linear algebra is unavoidable, and pretending otherwise only delays the work. State vectors, operators, and measurement are things I want to reason about directly rather than treat as API calls.
Then write small things
A few lines in Qiskit that build a circuit, draw it, and inspect the statevector teach more than a long tutorial I skim. The point is to predict the result before running the code.
Then reuse it in projects
Once a concept survives a small experiment, I look for a place where it earns its keep. That is usually where the real learning happens, because the project forces decisions that a clean tutorial example avoids.