Documentation

CsdLean4.SigmaLayer.ContextFixedA7

Context-fixed A7 at general N: the support reduction #

Category: SigmaLayer (the Paper C A7 architecture).

Paper C A7 asks for outcome regions Ωᵢ(M) ⊂ ℂℙⁿ⁻¹ fixed by the apparatus context M alone, together with a preparation law ρ_ψ^ep, such that

P(i ∣ M, ψ) = ∫_{Ωᵢ(M)} ρ_ψ^ep dμ_FS = |⟨eᵢ|ψ⟩|².

Under U(N)-covariance the preparation density collapses to ρ_ψ^ep(φ) = g(|⟨ψ|φ⟩|²) for a single function g : [0,1] → ℝ≥0, the same for every state and every context. So the whole question is whether such a g exists. At N = 2 it does — g(s) = 4(2s−1)₊, the CSD spread density, with {Ωᵢ} the hemispheres (LF4/QubitBorn.lean qubitBorn). At N ≥ 3 the question is open in both directions (specs/BACKLOG.md; the earlier "provably dead" verdict rested on numerics plus an informal argument and was retracted 2026-07-28).

What this file contributes #

The support reduction: a hard, elementary constraint on any such g, obtained by evaluating the A7 requirement at the n basis-vector preparations ψ = eⱼ, where the Born weights are |⟨eᵢ|eⱼ⟩|² = δᵢⱼ. For i ≠ j the requirement says a non-negative integrand integrates to zero over Ωᵢ, which forces it to vanish a.e. there. Consequently

Read physically: a base-only preparation density must be concentrated, with the region it occupies shrinking as 1/n, while still integrating to 1. It has to spike. That is a genuine structural obstruction — it is what the N = 2 solution 4(2s−1)₊, supported exactly on (½, 1], is doing — and it sharply narrows the space any N ≥ 3 construction must live in.

Deliberately stated over an abstract measure space #

Nothing here needs projective geometry: it is the measure-theoretic core, so it is proved once, for a probability space with n overlap functions and n disjoint regions. The intended instantiation is X := CPN n, μ := fubiniStudyMeasure p₀ (a probability measure), s j := momentMap · j (momentMap_mk_eq_inner_sq identifies it with |⟨eⱼ|φ⟩|², and momentMap_sum_eq_one gives ∑ⱼ sⱼ = 1), Ω j := Ωⱼ(M). Keeping it abstract also means the result survives a move to a fibred Σ, where the same reduction applies verbatim.

What this does NOT do #

It is not the no-go. It constrains g; it does not refute it. Two things stand between this and a genuine N ≥ 3 impossibility theorem:

  1. The generic-ψ requirement is untouched. Everything here comes from the n basis-vector preparations. The suspected obstruction lives at generic ψ, where the Born value |⟨eᵢ|ψ⟩|² varies continuously and the cap around ψ straddles several regions.
  2. The harmonic argument is out of reach. The informal reason for pessimism is that g(|⟨ψ|φ⟩|²) integrated over a fixed region produces, as a function of ψ, components of every degree (k,k), while the target |⟨eᵢ|ψ⟩|² is pure degree (1,1); killing the higher harmonics for all regions and all contexts is what should fail at N ≥ 3. Formalising that needs representation theory / harmonic analysis on ℂℙⁿ⁻¹ for which Mathlib has no API.

So: this is step one of the no-go, and it is honest about being step one.

References #

LF4/QubitBorn.lean (qubitBorn, the N = 2 case that does work); LF4/MomentMap.lean (momentMap_mk_eq_inner_sq, momentMap_sum_eq_one); specs/BACKLOG.md (the re-opened general-N A7 row, and the hpos boundary row — note the DH region machinery cannot express a vanishing amplitude, a second structural limit at this same spot); specs/record-layer-plan.md §3; specs/sigma-fibre-contextuality.md.

def CSD.SigmaLayer.overlapSupport {X : Type u_1} (g : ) (s : X) :
Set X

The set where the preparation density is non-zero at overlap function s: the ontic states that a preparation with overlap profile s can actually occupy.

Equations
Instances For
    theorem CSD.SigmaLayer.measurableSet_overlapSupport {X : Type u_1} [MeasurableSpace X] {g : } {s : X} (h : Measurable fun (x : X) => g (s x)) :

    The reduction #

    theorem CSD.SigmaLayer.ae_eq_zero_of_setIntegral_eq_zero {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {g : } {s : X} {Ω : Set X} (hg : ∀ (t : ), 0 g t) (hint : MeasureTheory.IntegrableOn (fun (x : X) => g (s x)) Ω μ) (hzero : (x : X) in Ω, g (s x) μ = 0) :
    ∀ᵐ (x : X) μ.restrict Ω, g (s x) = 0

    A non-negative density with a vanishing Born weight vanishes on that region.

    This is the whole mechanism, isolated. The A7 requirement at preparation eⱼ and outcome i ≠ j reads ∫_{Ωᵢ} g(sⱼ) dμ = |⟨eᵢ|eⱼ⟩|² = 0; since g ≥ 0, a zero integral forces the integrand to vanish almost everywhere on Ωᵢ. Non-negativity of the preparation density is doing all the work — exactly the hypothesis a signed density would escape.

    theorem CSD.SigmaLayer.overlapSupport_ae_subset {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } {g : } {s : Fin nX} {Ω : Fin nSet X} (hg : ∀ (t : ), 0 g t) (hint : ∀ (i j : Fin n), MeasureTheory.IntegrableOn (fun (x : X) => g (s j x)) (Ω i) μ) (hoff : ∀ (i j : Fin n), i j (x : X) in Ω i, g (s j x) μ = 0) (hcover : ∀ (x : X), ∃ (i : Fin n), x Ω i) (hmeas : ∀ (i : Fin n), MeasurableSet (Ω i)) (j : Fin n) :
    μ (overlapSupport g (s j) \ Ω j) = 0

    The support of the density for outcome j lies inside region j (up to a null set).

    Given the off-diagonal A7 conditions — the Born weight of outcome i at preparation eⱼ is zero for i ≠ j — and a covering family of regions, the states where g(sⱼ) ≠ 0 cannot lie in any region other than Ωⱼ.

    theorem CSD.SigmaLayer.overlapSupports_ae_disjoint {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } {g : } {s : Fin nX} {Ω : Fin nSet X} (hg : ∀ (t : ), 0 g t) (hint : ∀ (i j : Fin n), MeasureTheory.IntegrableOn (fun (x : X) => g (s j x)) (Ω i) μ) (hoff : ∀ (i j : Fin n), i j (x : X) in Ω i, g (s j x) μ = 0) (hcover : ∀ (x : X), ∃ (i : Fin n), x Ω i) (hmeas : ∀ (i : Fin n), MeasurableSet (Ω i)) (hdisj : Pairwise (Function.onFun Disjoint Ω)) {j k : Fin n} (hjk : j k) :
    μ (overlapSupport g (s j) overlapSupport g (s k)) = 0

    The supports are pairwise almost disjoint. Distinct outcomes' preparation supports cannot overlap on a set of positive measure: each is confined to its own region, and the regions are disjoint.

    theorem CSD.SigmaLayer.sum_measure_overlapSupport_le_one {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } [MeasureTheory.IsProbabilityMeasure μ] {g : } {s : Fin nX} {Ω : Fin nSet X} (hg : ∀ (t : ), 0 g t) (hint : ∀ (i j : Fin n), MeasureTheory.IntegrableOn (fun (x : X) => g (s j x)) (Ω i) μ) (hoff : ∀ (i j : Fin n), i j (x : X) in Ω i, g (s j x) μ = 0) (hcover : ∀ (x : X), ∃ (i : Fin n), x Ω i) (hmeas : ∀ (i : Fin n), MeasurableSet (Ω i)) (hdisj : Pairwise (Function.onFun Disjoint Ω)) :
    j : Fin n, μ (overlapSupport g (s j)) 1

    The supports occupy at most the whole space, hence at most 1/n each by symmetry.

    The quantitative form of the reduction: a base-only preparation density is confined to n pairwise-disjoint sets whose measures sum to at most 1. Each must still carry total integral 1, so as n grows the density has to spike on an ever-smaller set.

    Step two: the cap — the density is confined above ½ #

    The supports are Aᵢ = {φ : sᵢ(φ) ∈ S_g} for S_g = {t | g t ≠ 0}, and step one showed them pairwise a.e. disjoint. Now push on that.

    Suppose S_g contained a positive-measure set T of overlap values below ½. Two coordinates taking values in T sum to less than 1, so such states are not excluded by ∑ᵢ sᵢ = 1 — and at N ≥ 3 there is a third coordinate free to absorb the remainder, so they occur with positive measure. Any such state lies in Aⱼ ∩ Aₖ, contradicting disjointness.

    Conclusion: g vanishes a.e. below ½. The N = 2 solution 4(2s−1)₊ is supported exactly on (½, 1], so the bound is sharp and attained.

    The state-abundance input is taken as an explicit hypothesis (hjoint) rather than derived, because deriving it is the Dirichlet pushforward of μ_FS — real work, and orthogonal to the argument. What makes this worth stating that way is joint_degenerate_of_sum_eq_one below: the hypothesis fails at N = 2, and that failure is exactly the qubit's escape route.

    theorem CSD.SigmaLayer.cap_of_joint_nondegenerate {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } {g : } {s : Fin nX} {j k : Fin n} (hgm : Measurable g) (hdisj : μ (overlapSupport g (s j) overlapSupport g (s k)) = 0) (hjoint : ∀ (T : Set ), MeasurableSet TTSet.Ioo 0 (1 / 2)0 < MeasureTheory.volume T0 < μ {x : X | s j x T s k x T}) :
    MeasureTheory.volume ({t : | g t 0} Set.Ioo 0 (1 / 2)) = 0

    The cap bound. If two overlap coordinates can jointly take values in any positive-measure set below ½, then the preparation density vanishes almost everywhere below ½.

    theorem CSD.SigmaLayer.joint_degenerate_of_sum_eq_one {X : Type u_1} {n : } {s : Fin nX} {j k : Fin n} (hsum : ∀ (x : X), s j x + s k x = 1) {T : Set } (hT : TSet.Iio (1 / 2)) :
    {x : X | s j x T s k x T} =

    Why the qubit escapes. When two overlap coordinates are complementary — sⱼ + sₖ = 1, which is forced at N = 2 because the two Born weights exhaust the state — they can never both lie below ½. So the abundance hypothesis of cap_of_joint_nondegenerate fails identically, and no cap bound follows.

    This is not a technicality: it is the structural reason N = 2 admits a base-only density at all. At N ≥ 3 the coordinates stop being functionally dependent, a third one is free to absorb the remainder, and the escape closes.

    theorem CSD.SigmaLayer.base_only_density_confined {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } [MeasureTheory.IsProbabilityMeasure μ] {g : } {s : Fin nX} {Ω : Fin nSet X} (hg : ∀ (t : ), 0 g t) (hint : ∀ (i j : Fin n), MeasureTheory.IntegrableOn (fun (x : X) => g (s j x)) (Ω i) μ) (hoff : ∀ (i j : Fin n), i j (x : X) in Ω i, g (s j x) μ = 0) (hcover : ∀ (x : X), ∃ (i : Fin n), x Ω i) (hmeas : ∀ (i : Fin n), MeasurableSet (Ω i)) (hdisj : Pairwise (Function.onFun Disjoint Ω)) {j k : Fin n} (hjk : j k) (hgm : Measurable g) (hjoint : ∀ (T : Set ), MeasurableSet TTSet.Ioo 0 (1 / 2)0 < MeasureTheory.volume T0 < μ {x : X | s j x T s k x T}) :
    MeasureTheory.volume ({t : | g t 0} Set.Ioo 0 (1 / 2)) = 0 i : Fin n, μ (overlapSupport g (s i)) 1

    The reduction, assembled. Under the hypotheses of step one plus joint non-degeneracy of two overlap coordinates, a base-only U(N)-covariant non-negative preparation density is confined to overlap values ≥ ½ — up to a null set — while still having to integrate to 1.

    Together with sum_measure_overlapSupport_le_one this is a genuine squeeze on any N ≥ 3 construction: the density lives on a set of measure ≤ 1/n and only where the overlap exceeds ½. It is not yet a contradiction; see the module header for the two steps that remain.

    Step four: orthogonal preparations, and the (n−1)/n support bound #

    Steps one to three used only the n basis-vector preparations. The first genuinely generic-ψ input is the orthogonal one: whenever ψ ⊥ eᵢ the Born weight |⟨eᵢ|ψ⟩|² is zero, so the same non-negativity argument applies — g ∘ sψ must vanish a.e. on Ωᵢ.

    What makes this stronger than the basis-vector case is that there are many such ψ, and the overlap values they realise at a given φ sweep an entire interval. For unit ψ ⊥ eᵢ, the overlap |⟨ψ|φ⟩|² is maximised at the normalised projection of φ into eᵢ^⊥, with value 1 − sᵢ(φ); tilting ψ within eᵢ^⊥ scales it continuously down to 0. That tilt needs dim eᵢ^⊥ ≥ 2, i.e. n ≥ 3 — at n = 2 the orthocomplement is a line, ψ is unique up to phase, and only the single value 1 − sᵢ(φ) is realised. The same threshold, for the third independent reason.

    So for n ≥ 3: a.e. φ ∈ Ωᵢ forces g ≡ 0 on the whole interval [0, 1 − sᵢ(φ)]. Since the regions cover, a.e. φ has some i with g ≡ 0 on [0, 1 − sᵢ(φ)], and maxᵢ sᵢ(φ) can be made arbitrarily close to 1/n on a positive-measure set — so g vanishes below (n−1)/n.

    At n = 2 that reads g = 0 below ½, which is exactly where 4(2s−1)₊ is supported. The bound is sharp at the one dimension where a solution is known.

    theorem CSD.SigmaLayer.orthogonal_preparation_vanishes {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {g : } {Ω : Set X} {u : X} (hg : ∀ (t : ), 0 g t) (hint : ∀ (m : ), MeasureTheory.IntegrableOn (fun (x : X) => g (u m x)) Ω μ) (hzero : ∀ (m : ), (x : X) in Ω, g (u m x) μ = 0) (hmeas : MeasurableSet Ω) :
    ∀ᵐ (x : X) μ, x Ω∀ (m : ), g (u m x) = 0

    Orthogonal-preparation confinement. If a family of preparations all give outcome i zero Born weight, then on Ωᵢ the density vanishes at every overlap value any of them realises.

    Only the non-negativity of g and the vanishing of the Born weight are used, so this is the basis-vector argument of step one applied to a much larger family — the first step that uses preparations other than the eⱼ.

    theorem CSD.SigmaLayer.vanishes_on_interval_of_dense {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {g : } {Ω : Set X} {u : X} {r : X} (hgc : Continuous g) (hvan : ∀ᵐ (x : X) μ, x Ω∀ (m : ), g (u m x) = 0) (hdense : xΩ, tSet.Icc 0 (r x), ε > 0, ∃ (m : ), |u m x - t| < ε) :
    ∀ᵐ (x : X) μ, x ΩtSet.Icc 0 (r x), g t = 0

    The interval form. If, at almost every x ∈ Ω, the realised overlap values are dense in [0, r x] and g is continuous, the vanishing extends from those values to the whole interval.

    The density hypothesis is the geometric input: for ψ ranging over the unit sphere of eᵢ^⊥, the overlaps |⟨ψ|φ⟩|² sweep [0, 1 − sᵢ(φ)] — available exactly when dim eᵢ^⊥ ≥ 2, i.e. n ≥ 3.

    Step five: the (n−1)/n support bound #

    Step four gives, for a.e. φ, an outcome i with g ≡ 0 on [0, 1 − sᵢ(φ)]. To make that a statement about g alone, take φ as balanced as possible: the smaller maxᵢ sᵢ(φ), the longer the interval. Since ∑ᵢ sᵢ = 1 forces maxᵢ sᵢ ≥ 1/n, the best available interval is [0, 1 − 1/n], and states arbitrarily close to the barycentre occur with positive measure.

    The step is short because step four's conclusion is pointwise in gg vanishes on a whole interval, for a.e. φ. g is a fixed function, so one suitable φ suffices; no almost- everywhere bookkeeping survives into the conclusion.

    theorem CSD.SigmaLayer.exists_mem_of_measure_pos_of_ae {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {A : Set X} {P : XProp} (hA : 0 < μ A) (hP : ∀ᵐ (x : X) μ, P x) :
    xA, P x

    A positive-measure set meets any almost-everywhere property.

    theorem CSD.SigmaLayer.vanishes_below_of_balanced {X : Type u_1} [MeasurableSpace X] {μ : MeasureTheory.Measure X} {n : } {g : } {s : Fin nX} (_hn : 0 < n) (hvan : ∀ᵐ (x : X) μ, ∃ (i : Fin n), tSet.Icc 0 (1 - s i x), g t = 0) (hbalanced : ∀ (c : ), 1 / n < c0 < μ {x : X | ∀ (i : Fin n), s i x c}) (t : ) :
    t Set.Ico 0 (1 - 1 / n)g t = 0

    ★ The (n−1)/n support bound. A base-only preparation density vanishes on every overlap value below (n−1)/n.

    hvan is what step four delivers (each state lies in some outcome region, and there g dies on [0, 1 − sᵢ]); hbalanced says states with all overlaps below c are not negligible, for any c above the forced minimum 1/n.

    At n = 2 the bound reads "g vanishes below ½", which is exactly the support of the known solution 4(2s−1)₊ — sharp at the one dimension where a solution exists.