Evidence gates, or how to decide whether quantum earns its place
Most quantum programmes have no defined condition under which they would stop. Four gates fix that.
Systems 3 min read 
Ask a quantum programme what would have to be true for them to abandon the quantum path, and watch what happens. Most cannot answer. Not because the people are unserious, but because the question was never posed at the start, and by the time it becomes uncomfortable there is too much invested to pose it.
I wanted a structure that made the answer available from day one. Four gates, each with a condition that can be failed.
G0, define
The workload has to be explicit, measurable, latency-tolerant and safe to run outside local control.
Most candidate workloads fail here, and failing here is cheap. If you cannot state the workload precisely enough to measure it, everything downstream is going to be an argument about interpretation. If it is latency-critical, current hardware access patterns rule it out regardless of how elegant the algorithm is. If it cannot safely leave local control, a cloud quantum backend is not available to you.
Getting a clear no at G0 costs a week. Getting it at G3 costs a year.
G1, simulate
The alternative method has to produce valid results against a strong classical reference.
The word doing the work in that sentence is strong. Comparing a quantum method against a deliberately weak classical baseline is the most common way this field produces results that do not survive contact with a competent reviewer. The baseline has to be the one a capable engineer would actually build if you told them to solve the problem without quantum.
In the QANTIS work the reference is exact Bayesian inference, which is the correct comparison and also an unforgiving one.
G2, preserve
Hardware execution has to preserve information, constraints and uncertainty.
This gate exists because of a specific failure mode. A method can appear to succeed by discarding the thing that made the problem hard. If the uncertainty representation does not survive execution, you have not solved the inference problem, you have replaced it with a different and easier one. The comparison is then meaningless even if the numbers look good.
G3, qualify
The complete path has to add decision value after accuracy, latency, reliability and cost are all counted.
Everything is in scope at G3: the queue time, the calibration drift, the cost per shot, the operational complexity of maintaining a second execution path. A method can be genuinely more accurate and still fail this gate, because accuracy is not the only currency.
Why publish the gates
Two reasons.
The first is internal. A team that knows the exit conditions argues about evidence rather than about enthusiasm. When a result comes in below the bar, the conversation is short.
The second is external. If I tell you that the quantum pathway in NODERIQ is conditional, that statement is worth very little unless I also tell you what the condition is. Published gates make the claim checkable. Someone can read them, look at what has been published, and form their own view about which gates have actually been cleared.
Right now the honest answer is that G0 and G1 are addressed for the belief-update workload, G2 is what the hardware campaigns in both QANTIS papers were built to test, and G3 is not settled. The conditional quantum pathway is stage three of a three-stage roadmap for a reason. Stage one is a classical AI core, and it comes first because it has to work whether or not stage three ever does.