Executable classical Boltzmann-style sampling illustration; not QAOA or quantum measurement.
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Research Sweep Guide: Read Penalty, Feasible Fraction and Sample Budget Honestly
Open the research sweep page for a short interpretation exercise. Before pressing either button, read the disclosure: this implementation is a classical energy-weighted sampler. It has no executable quantum circuit or quantum measurement path. Use the Resilience Lab when your question specifically concerns its simulated QAOA circuit.
Step 1: Write down the backend and the toy
Record the page URL, the date and the fact that the backend is a classical Boltzmann-style sampler. The fixed toy chooses exactly two links from six candidates and evaluates a coverage-related energy. It is not an imported network, a satellite service or a simulation of real operational traffic.
Keep this model description beside any screenshot or copied table. Without it, the terms penalty, feasible and optimum can suggest a stronger experiment than the implementation supports. The page's use of the same general toy as another lab does not prove identical treatment of all infeasible states.
Step 2: Run the penalty sweep
Press Run penalty sweep. The displayed experiment uses seed 42 and 512 classical samples while varying the penalty coefficient. Read the penalty value, feasible percentage, best coverage and optimum-hit columns as separate observations.
The feasible percentage is based on the sample count meeting the exactly-two selection rule. Best coverage concerns an observed valid selection. If no valid selection appears, an absent best score must not be replaced with the reference answer just to fill the table.
Step 3: Explain one row without overclaiming
Describe a result in the form: under this classical distribution, coefficient and sample budget, this fraction of samples satisfied the count constraint, and the best observed feasible coverage had this value. Do not describe it as a QAOA success probability or evidence of a quantum advantage.
If a larger penalty produces more feasible samples, that is an observation about the implemented weighting. It does not by itself establish better ranking among the feasible choices, a robust optimum for a different instance or a reliable operational network design.
Step 4: Run the sample-budget sweep
Press Run shot sweep. Read the surrounding text that qualifies the historical research label: the draws here are classical samples. The page keeps the penalty and seed fixed while varying the budget. A change in best observed coverage can result from seeing more draws from the same distribution.
The entries are not independent hardware executions. With the deterministic generator and a repeated seed, longer draws can share the earlier sequence. Treat the rows as a controlled budget illustration, not a collection of independent trials suitable for an unqualified confidence interval.
Step 5: Keep a compact experiment log
Record the model/backend, penalty, seed, number of samples, feasible count or fraction, best feasible objective and the reference used for the optimum-hit comparison. Separate what the page displayed from your interpretation of it. If you repeat the exercise, retain both observations rather than silently replacing an inconvenient result.
There is no claim that the page measures runtime on a quantum processor or compares hardware and classical costs. A screenshot of a percentage is not sufficient evidence for either claim. The QAOA paper is useful background for understanding why a real circuit workflow has additional requirements.
Step 6: Choose the correct next activity
To inspect executed circuit evidence, move to the QAOA lab guide. To plan new work such as noise modeling or seed variance, use the research roadmap guide, which distinguishes proposals from implemented controls.
This exercise is successful when you can explain the table without calling a classical sample a quantum measurement. No new result needs to be invented, and no benchmark needs to be described as completed simply because a research idea has a name.