Signed commutation of the point observables, on the anticommuting tuples #
Blueprint lem:qld-obs-commutation, the anticommuting case. On a tuple omega with
gamma(omega) = 1 the two point observables anticommute on the state:
X^{r_X}(u_X) Z^{r_Z}(u_Z) (x) Id ~ - Z^{r_Z}(u_Z) X^{r_X}(u_X) (x) Id .
Three inputs, all already proved:
- item 7 of
lem:qld-win(item_ms_consistency_X,item_ms_consistency_Z): each point probe agrees with one of the two distinguishedVariablebits on the other side. At the level of observables that isX (x) Id ~ Id (x) V_1andZ (x) Id ~ Id (x) V_5, at344 epseach --- item 7 at the two-outcome probe, thenxStateDist_obsOf_le; - item 6 (
item_magicSquare): those twoVariableobservables anticommute on the state, at16049664 eps; - the order reversal
BipartiteModel.stateNorm_anticomm_le, which is what carries an anticommutation from Bob's side to Alice's --- and is where the sign comes from.
Why this case costs eps and not sqrt(eps) #
The paper states lem:qld-obs-commutation at O(sqrt(eps)), and that is what its commuting
case costs: it needs the Pair measurement to be projective, which a general POVM strategy's is
not, so it goes through the orthogonalization corollary (cor:ortho-from-consistency) and pays a
square root. Here the first player's measurements are projective by hypothesis, and the blueprint
states the lemma at the constant proved below. The anticommuting case needs no projectivity: every input is already a squared-norm bound linear in eps, and the
only inequalities used are the triangle inequality and (u+v+w)^2 <= 3(u^2+v^2+w^2). So the
constant here is honest and there is no square root --- 48157248 eps, from
12 * 344 + 12 * 344 + 3 * 16049664.
The commuting case below is linear in eps as well: the strategy's Pair measurement is
projective, so no orthonormalization, and no square root, enters either half.
In a bipartite model #
Stated in a bipartite model (Phase 5 of planning/mipco-track.md). The strategy is a model
M : BipartiteModel ๐ ๐ โฌ with โM.ฯโ = 1 and two families of POVMs PA, PB in the players'
ordered algebras, the first of them projective (IsPVMIn), as every strategy of the Pauli
basis test is. The observables are elements of the players' algebras --- a two-outcome POVM's
ยฑ1-observable is the difference of its two elements, POVMIn.obs2 --- their norms on the state
are M.stateSqNorm, M.swap.stateSqNorm and M.xSqNorm, and a two-outcome observable is a
contraction on the Hilbert space (M.Bnd, from BipartiteModel.bnd_ฯA_sub) rather than in the
algebra, which need not have a functional calculus.
The first player's projectivity is used twice: by item 6 (item_magicSquare, through
lem:ms-direct-anticomm), and in the commuting case, where Alice's joint Pair measurement is a
coarse-graining of her projective Pair measurement and so is projective itself
(isPVMIn_pairPOVM). That is the projective measurement the commutation analysis needs, so the
matrix route's de la Salle orthonormalization (cor:ortho-from-consistency, which replaced that
POVM by a nearby projective measurement) is gone, and with it the slack ฮท its strict hypothesis
asked for. Every constant of the matrix statements is kept. On a unit vector
ฯ : dA ร dB โ โ the statements are read at the tensor-product model BipartiteModel.tensor ฯ
with the families POVM.toIn.
Two-outcome observables in an ordered โ-ring #
The model form of MIPRE.obs2: the ยฑ1-observable of a POVM with outcomes in F_2 is the
difference of its two elements.
The observable of a coarse-grained POVM, in terms of the original one.
The commutator of two two-outcome observables is four times that of their 1-elements,
exactly: an observable is 1 - 2 P_1, and the identity commutes with everything. So passing from
the POVM elements to the observables costs a factor 16 in a squared norm and no
approximation.
The two observables #
Alice's point observable: the ยฑ1-observable of the trace probe of the (Point, W)
answer against the content's own r_W. This is the blueprint's W^{r_W}(u_W).
Equations
- MIPRE.QLD.ptObs hm P W c = (MIPRE.POVMIn.map (MIPRE.QLD.rdProbeAt W c) (P (MIPRE.QLD.Content.question hm c (MIPRE.QLD.Ty.point W)))).obs2
Instances For
Bob's variable observable: the ยฑ1-observable of the bit a Variable_j answer
reports.
Equations
- MIPRE.QLD.varObs hm P j c = (MIPRE.POVMIn.map MIPRE.QLD.rdBit (P (MIPRE.QLD.Content.question hm c (MIPRE.QLD.Ty.var j)))).obs2
Instances For
Alice's point observable is a contraction on the Hilbert space: it is a difference of two POVM elements, with no projectivity.
Bob's variable observable is a contraction on the Hilbert space.
The weight: uniform on the anticommuting tuples #
The weight the items of lem:qld-win carry on the anticommuting tuples: uniform over all
contents, supported on acommSet. It is not a probability distribution --- its total mass is the
fraction of tuples that anticommute --- which is exactly how the items are stated.
Equations
- MIPRE.QLD.acommWeight F m c = if c โ MIPRE.QLD.acommSet then (โ(Fintype.card (MIPRE.QLD.Content F m)))โปยน else 0
Instances For
Item 7 at the level of observables #
Item 7 is a POVM-level agreement: the point probe and the Variable bit take the same value. The
observable form is xStateDist_obsOf_le at the weighting sgn, whose cost is the number of
outcomes of the probe --- two --- so 172 eps becomes 344 eps. Exactly the step
pts_obs_consistency makes for item 1, with the weight supported on the anticommuting tuples.
The X point observable agrees with the Variable_1 observable across the two parties,
on the anticommuting tuples. Item 7 for X, then xStateDist_obsOf_le.
The Z point observable agrees with the Variable_5 observable across the two parties,
on the anticommuting tuples.
Item 6's anticommutator, in terms of the variable observables #
item_magicSquare states the bound for MS.anti (msPOVM PB omega), the anticommutator of the
conditional Magic Square strategy's two observables. Those are the same operators as
varObs: the conditional strategy at a Variable question is the Pauli test's measurement at
the corresponding question, with its answer relabelled to the bit it reports, and relabelling
along the injection Sum.inr does not change a coarse-grained POVM element.
The conditional Magic Square strategy's variable observables are the varObs.
Item 6's anticommutator is the anticommutator of the two point-tied variable observables.
The lemma #
The pointwise step: at a single content, the two point observables anticommute up to twice
each cross-party deviation plus Bob's own anticommutator. stateNorm_anticomm_le at the four
contraction bounds, squared by (a+b+c)^2 <= 3(a^2+b^2+c^2).
The anticommuting half of lem:qld-obs-commutation. On the anticommuting tuples the two
point observables anticommute on the state --- which is the blueprint's (-1)^{gamma(omega)} at
gamma = 1 --- at error 48157248 eps, with no square root. The first player's measurements are
projective, for item 6.
The inconsistency of a pair of POVMs #
The weight of the outcome pairs on which the first player's Q and the second player's R
differ. On a unit vector it is the disagreement BipartiteModel.dis (sum_diag_eq_one_sub),
written as the off-diagonal sum, which is nonnegative without a normalization; the
question-averaged BipartiteModel.inconsistency is the weighted sum of these.
The inconsistency of one pair of POVMs in a bipartite model.
Equations
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The diagonal terms sum to 1 - ฮณ, because the Born probabilities of a pair of POVMs sum
to one on a unit vector.
The commuting case #
The other half of lem:qld-obs-commutation. Here the two point observables commute, and the
route is the paper's: chain items 5, 4 and 1 so that each point probe is cross-party close to a
marginal of the Pair measurement on the other side, and apply the commutation analysis. The
paper first replaces that measurement by a projective one (cor:ortho-from-consistency); here it
is projective already, being a coarse-graining of a projective measurement
(isPVMIn_pairPOVM).
Two departures from the paper's bookkeeping, both forced by which side things live on.
The analysis is run on Bob's observables and transferred back. commutation_analysis wants the
two commuting families on one side and the joint projective measurement on the other, and the
projective joint measurement at hand is Alice's. So the analysis is applied to Bob's point
measurements against Alice's joint Pair measurement, in the swapped model M.swap, and the
conclusion is carried to Alice's observables by the order-reversal rule
(BipartiteModel.stateNorm_comm_le) with the point observables' cross-party consistency. This half
therefore asks nothing of Bob's measurements, and it keeps the matrix route's constant.
The chain is run in the mirrored orientation. Every rule of fig:decider_pauli is stated in
both orientations, so items 5 and 1 are available with the players exchanged; item 4 is used as it
stands. The chain is
A^{Pair}_{[beta_W = o]} ~ B^{Pair}_{[beta_W = o]} ~ A^{(Pair,W)}_o ~ B^{(Point,W)}_{probe = o},
three links at 172 eps each, so 1548 eps after one three-term triangle inequality.
Alice's joint Pair measurement: both bits at once, which is the product outcome set the
commutation analysis needs.
Equations
- One or more equations did not get rendered due to their size.
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Alice's point-probe measurement, of which ptObs is the observable.
Equations
- MIPRE.QLD.ptPOVM hm P W c = MIPRE.POVMIn.map (MIPRE.QLD.rdProbeAt W c) (P (MIPRE.QLD.Content.question hm c (MIPRE.QLD.Ty.point W)))
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Bob's joint Pair measurement.
Equations
- One or more equations did not get rendered due to their size.
Instances For
Bob's point-probe measurement.
Equations
- MIPRE.QLD.ptPOVMB hm P W c = MIPRE.POVMIn.map (MIPRE.QLD.rdProbeAt W c) (P (MIPRE.QLD.Content.question hm c (MIPRE.QLD.Ty.point W)))
Instances For
Bob's point observable.
Equations
- MIPRE.QLD.ptObsB hm P W c = (MIPRE.QLD.ptPOVMB hm P W c).obs2
Instances For
Bob's point observable is a contraction on the Hilbert space.
Alice's joint Pair measurement is projective when her strategy is: it is a
coarse-graining of her projective Pair measurement. This is what replaces the matrix route's
orthonormalization.
The W-marginal of the joint Pair measurement is the Pair measurement read in W
alone.
The three links #
Item 5 in the mirrored orientation: Alice's (Pair, W) bit agrees with Bob's point
probe.
Item 1 at the Pair type, read in one basis, restricted to the commuting tuples.
The chain (the paper's eq:lc-11, mirrored): Alice's Pair measurement read in one basis
is cross-party close to Bob's point probe, on the commuting tuples. Three links at 172 eps, one
three-term triangle inequality.
The three averaged inputs #
The per-content quantity chain_pair_point bounds.
Equations
- One or more equations did not get rendered due to their size.
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The inconsistency of the two players' joint Pair measurements at one content.
Equations
- MIPRE.QLD.incQty hm M PA PB c = MIPRE.QLD.pairInconsistency M (MIPRE.QLD.pairPOVM hm PA c) (MIPRE.QLD.pairPOVMB hm PB c)
Instances For
The weight the items carry on the commuting tuples.
Equations
- MIPRE.QLD.commWeight F m c = if c โ MIPRE.QLD.commSet then (โ(Fintype.card (MIPRE.QLD.Content F m)))โปยน else 0
Instances For
The point observables are cross-party consistent at the content's own probe. Item 1 at the
type (Point, W) with the reading rdProbeAt W, which is a family --- the probe depends on the
content --- and then xStateDist_obsOf_le. pts_obs_consistency is the same statement at a fixed
r; here the probe is the one the commutation check uses.
The inconsistency of the two joint Pair measurements is a conditional failure. Item 1
again, at the Born level, which is where data processing is available: fact:data-processing is a
statement about consistency, and NW19's own remark gives a counterexample for the
state-dependent distance.
The per-content bound #
The inputs meet here: chainQty is what the chain bounds, and incQty, the inconsistency of the
two players' joint Pair measurements, is the term through which the matrix route paid for
projectivizing Alice's Pair measurement --- 18 times it from cor:ortho-from-consistency, and
a factor 2 for each marginal. That measurement is projective here, so the term costs nothing;
it is kept with its weight, so that every constant of the matrix statements survives, and so is
its bound sum_incQty_le. The slack ฮท the orthonormalization's strict hypothesis asked for, and
the limit that removed it, are gone.
The per-content bound. At one content, the two point observables commute on the state up
to the chain, the inconsistency term, and the two cross-party consistencies of the observables
themselves. 768 = 3 * 256: the analysis contributes 16, the observable expansion 16, and the
final triangle inequality 3.
The commuting half of lem:qld-obs-commutation. On the commuting tuples the two point
observables commute on the state, at 9519168 eps --- and, like the anticommuting half, with no
square root. The first player's measurements are projective, so that her joint Pair measurement
is.
Both halves #
lem:qld-obs-commutation as the paper states it: one average over all contents, with the sign
(-1)^{gamma(omega)} inside. The two halves are the two branches of that sign, and the type graph's
content distribution is uniform, so the statement is the sum of the two restricted ones.
lem:qld-obs-commutation. On average over the verifier's content, the two point
observables commute up to the sign (-1)^{gamma(omega)}, at 57676416 eps = 9519168 eps + 48157248 eps. The paper states this at O(sqrt(eps)); it is O(eps), and the two halves are
where that is decided. The first player's measurements are projective, which both halves use.