By Lunibyte Team · November 1, 2026 · 4 min read · Guide

Classical illustration panels beside planned circuit studies; angle results remain unavailable.

Research Roadmap Guide: How to Plan Five Quantum-Lab Experiments Without Fake Metrics

Use the current classical illustration buttons to inspect sampling behavior, then use this protocol to review proposed circuit experiments. The five November 1 research notes are proposals: simple noise, warm starts, constraint handling, angle sensitivity and seed variance. The public page has classical mixture, distribution, filtering and seed tables; those are not quantum experiment results. Angle sensitivity is unavailable.

Step 1: Establish an executable reference

Use the existing Resilience Lab and beginner circuit guide to identify a real local simulated-circuit run. Record the toy instance, backend, depth, shots, seed, angles, objective definition, feasible fraction and separately computed classical reference. Keep the exported evidence when the lab offers it.

A new study should name the reference it changes. Do not borrow results from the classical sweep page and call them QAOA: that page uses an energy-weighted classical sampler. Similar-looking columns do not establish equivalent algorithms.

Step 2: Prepare the noise experiment specification

Write down the channel and where it is applied. The proposal discusses a simple depolarizing model with several candidate noise settings after the ideal circuit. Require a checked implementation before recording results. A random classical bit flip should not be silently substituted for the stated quantum channel.

Keep the ideal reference, angles and sample policy fixed for a clearly scoped comparison. Specify feasible yield and objective quality separately, including no-feasible cases. Label all conclusions as properties of the chosen mathematical noise model, not a measurement of a real processor.

Step 3: Make the warm-start information budget explicit

List exactly which classical information initializes the proposed method. If the exact toy optimum is supplied, say so before comparing evaluation counts. Include the effort required to obtain that information when discussing total work.

Define the starting method and stopping rule for both conditions. A method given the answer should not be promoted as an independently discovered speedup. This toy can help debug hybrid mechanics, but a broader benchmark would need cases where the optimum is not handed to the initialization.

Step 4: Define the constraint comparison precisely

Distinguish a penalty-based energy from post-selection after sampling. Record total draws, valid draws, discarded draws and the score among retained outcomes. Decide whether an unconditional or conditional quantity is being compared and keep the denominator visible.

If the goal is a constraint-preserving circuit, a different mixer or state preparation must actually be implemented and tested. Post-selection alone does not establish that property. First check existing reporting so the experiment does not claim a novelty that is already the lab's best-feasible selection rule.

Step 5: Specify the angle perturbations and metrics

Record the returned angles and the local optimizer settings. List the proposed positive and negative perturbations, changing one coordinate at a time for the initial diagnostic. The reference point is a returned local-search solution, not a certified global parameter optimum.

For each future evaluation, retain penalized expectation, feasible probability and any defined feasible-objective statistic separately. If the study reports only expected energy, do not rename that column expected coverage. A metric definition is part of the experiment, not an optional caption added afterward.

Step 6: Predefine the seed study

Hold the circuit and angles fixed while changing seeds under a fixed draw budget. The proposal suggests a group of twenty seeds; that is a planned circuit design, not twenty runs already executed on a quantum circuit. The current seed button instead executes twenty classical sampling sequences. Define how missing feasible samples affect min, median, maximum and hit-rate summaries before seeing the outcomes.

Preserve the per-seed records rather than publishing only the best seed. Do not claim independence or statistical certainty merely because the generator accepted different integers. Repeatability checks and sampling checks answer different questions.

Step 7: Decide what can be published

A completed experiment report should include executable implementation identity, validated model, raw outputs, parameters, reference checks, failures and interpretation limits. A proposal can be published now if it is labeled as a proposal and contains no invented measurements.

The QAOA paper is background, not evidence that these extensions ran. For the five questions in context, read the roadmap article. Until an implementation and its run evidence exist, keep the status planned rather than unavailable results disguised as zeroes.