Classical illustration panels beside planned circuit studies; angle results remain unavailable.
In this article
Five Next Questions for the Quantum Lab: A Research Roadmap, Not New Results
Five new notes propose ways to examine the six-qubit Resilience toy more carefully: noise, warm starts, constraint handling, angle sensitivity and seed variance. As of November 1, 2026, these are research proposals in the project documentation. The public research page now includes classical illustration panels for distribution mixing, biased sampling, post-selection and seed variation. These do not execute the proposed quantum studies; the angle experiment remains unavailable.
The distinction is useful rather than disappointing. A proposal should make its question, control conditions and required evidence clear before a chart is produced. The existing Resilience Lab supplies a bounded, inspectable simulated-circuit workflow and a classical reference; the notes describe questions that could extend that work.
1. A simple noise model
The first proposal asks how an explicitly specified depolarizing model might change feasibility and best observed coverage relative to an ideal circuit. It suggests several noise settings with a controlled sample budget. No resulting percentages or performance curves have been established by the note.
A final-state noise model would still be a limited mathematical assumption, not a reproduction of a particular processor. Its implementation must define the channel, distinguish it from a casual bit-flip shortcut and pass reference checks. Real gate, readout, connectivity and time-dependent errors cannot be inferred from that simple model.
2. A warm start informed by a classical solution
The warm-start note asks whether an initialization informed by classical work could reduce circuit evaluations on the toy. That is a hybrid research question, not a claim that the quantum part finds an answer unknown to the baseline.
Because an exact baseline can already know the toy optimum, using that answer to bias the start introduces privileged information. A fair report must disclose that information, count its classical cost and distinguish a toy diagnostic from a method usable on instances whose optima are unknown. Comparing final scores alone would conceal the main issue.
3. Soft penalties and filtering invalid samples
The constraint-handling proposal concerns the exactly-two selection rule. A penalty changes the objective energy; post-selection discards samples that fail a constraint after drawing them. Those are distinct operations.
Importantly, filtering invalid samples does not make the circuit preserve the constraint during execution. It is not the same as constructing a hard-constraint mixer. Report discarded samples, total attempts and the conditional quality of retained samples. In the current Resilience reporting, best observed feasible coverage already uses valid samples, so an implementation must define a genuinely different comparison rather than relabel an existing metric.
4. Sensitivity near returned angles
The angle note proposes perturbing optimized parameters and reevaluating the distribution. The current optimizer returns a bounded local-search result; calling it the global angle optimum would be stronger than the evidence allows.
A careful experiment should say which quantity changes. Penalized expectation energy, feasible probability and coverage conditional on valid outcomes are not interchangeable. Perturbing one parameter at a time can expose local sensitivity, but it does not map the whole landscape or establish robustness on hardware.
5. Seed-to-seed sampling variation
The seed proposal asks how a fixed budget, such as 512 draws, changes the best feasible outcome across a planned group of seeds. No min/median/max or optimum-hit rate is supplied as a completed result here.
Holding angles fixed separates sampling variation from a new optimization search. Repeating the same seed should reproduce the deterministic sampling sequence, while distinct seeds provide additional draws to inspect. The study must record any run with no feasible sample and avoid turning a small toy success rate into a general guarantee.
The current research page mixes a classical Boltzmann-style distribution with uniform probabilities, compares uniform and energy-biased sampling, filters invalid draws, and summarizes twenty classical seeds. Its noise mixture is not a per-qubit channel, its biased distribution is not a QAOA warm start, and its seed table is not a repeated circuit benchmark. Angle sensitivity stays disabled until actual circuit reevaluation exists.
Why this roadmap belongs beside a baseline
The original QAOA paper provides algorithmic background. Our existing guide to reading a QAOA run explains the local lab's evidence, and the classical sweep article identifies a separate illustration that is not QAOA.
The next useful milestone is a reproducible implementation and a complete record of its controls, not a more impressive label. The circuit extensions remain planned; the classical illustrations are available on the research page. The roadmap guide provides a protocol for reviewing proposed experiments without pretending their results already exist.