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DefinitionDefinition: AI-adaptedProof: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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Independent Local Quantum Fault Model

Definition

Let C be a finite channel-location schedule as in Finite ordered channel-location schedules, with finite ordered location set L(C). A location is a supplied gate, preparation, measurement, reset, or explicitly scheduled idle channel with declared support Sℓ. The model does not create idle locations that the schedule did not declare.

Fix η∈[0,1]. A fault pattern is a subset F⊆L(C). Its probability is

P(F)=η∣F∣(1−η)∣L(C)∖F∣,

where integer powers use the empty-product convention x0=1, including at x=0. Thus each location faults with probability η, and the event classes generated by the fault/no-fault indicator at each location form an independent family in the sense of Independent families of event classes. Conditional on F, each ℓ∈F replaces only the ideal operation at ℓ by an arbitrary CPTP map supported on Sℓ; all other locations apply their ideal channels. A replacement map may depend on the full pattern, but it acts only at its own location. Faults at measurement and reset locations are represented by channels on their declared quantum and classical registers.

This definition is the independent, local fault model: location indicators are independent, and each fault map acts at its own scheduled location.

Facts & Assumptions

Given: A finite location set L from a channel-location schedule and a parameter η∈[0,1]. Verify that the fault-pattern weights define a finite probability space and that the fault/no-fault event classes are independent.

[F1]

Finite ordered channel-location schedules: A schedule is a finite ordered list of location slots with full-register channels and declared supports.

[F2]

Independent families of event classes: Independence means that every finite choice of events from distinct event classes has intersection probability equal to the product of its probabilities.

[F3]

Finite probability spaces, outcome weights, events, and event probabilities: A finite probability space consists of a finite outcome set with nonnegative weights summing to one.

Verification

1.1givenF1F3chooseinductionalgebra

For every F⊆L, the displayed weight is nonnegative because η,1−η∈[0,1]. Its total mass is one by induction on ∣L∣: the empty location set has one pattern of weight 1; for nonempty L, choose ℓ0∈L and split the sum into patterns omitting and containing ℓ0. Each part is respectively (1−η) and η times the same normalized sum on L∖{ℓ0}, so their sum is one. The powers use the stated empty-product convention, including when η=0 or 1. Thus the weights define a finite probability space by [F3].

2.1givenF2step 1.1algebra∎

Fix distinct locations ℓ1,…,ℓk and prescribed fault/no-fault values bi∈{0,1}. Splitting a pattern into its restriction to these k locations and its restriction to the complement, then summing over all complement patterns using step 1.1, gives P(Xℓi=bi for all i)=∏i=1kηbi(1−η)1−bi. This is the product of the single-location atom probabilities. Summing over the finitely many atom choices belonging to any selected event in each indicator class proves the finite-subfamily independence condition [F2].

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Sources