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ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedverified 2026-10-22 (gpt-6.1-sol)
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Pauli six-outcome tomography

Example

On C2, define the Pauli matrices

σx=(0110),σy=(0−ii0),σz=(100−1).

Let I=I2, let H2={A∈M2(C):A=A∗}, and let Tr⁡ denote the diagonal-sum trace. For v∈C2, write v∗=vˉT. For A∈M2(C), A≥0 means A=A∗ and v∗Av≥0 for every v∈C2. A finite POVM is a finite family of positive semidefinite matrices whose sum is I; it is informationally complete when its real linear span is H2. The six matrices Fj,±=(I±σj)/6, with j∈{x,y,z}, form such an informationally complete POVM. For every ρ∈M2(C) with ρ≥0 and Tr⁡ρ=1, there are unique rx,ry,rz∈R such that ρ=(I+rxσx+ryσy+rzσz)/2. Define pj,±:=Tr⁡(ρFj,±); then rj=3(pj,+−pj,−). Form G(j,s),(k,t)=Tr⁡(Fj,sFk,t) and the canonical duals Dj,s=∑k,t(G+)(k,t),(j,s)Fk,t as in the preceding finite-frame definition. The linear-inversion procedure returns X(f)=∑j,sfj,sDj,s on a valid frequency vector, meaning fj,s≥0 and ∑j,sfj,s=1. It returns ρ when f=p. Some valid finite frequency vectors yield an output that is not positive semidefinite.

Facts & Assumptions

Given: The six displayed matrices and a density matrix ρ on C2.

[A1]

The Pauli matrices and effects are the finite matrices and operators in the Example.

[A2]

ρ is Hermitian, positive semidefinite, and has trace one.

[F1]

Tomography correctness and finite-sample stability: for a spanning finite POVM, exact probabilities reconstruct ρ, and the linear-inversion output is ∑afaDa for its canonical dual.

[F2]

The Gram pseudoinverse reconstructs a spanning Hermitian frame: for a positive semidefinite Gram matrix with spectral projections Pλ at positive eigenvalues, G+=∑λ>0λ−1Pλ.

[F3]

Finite Hermitian frames and their Gram pseudoinverse: Gab=Tr⁡(FaFb) and Da=∑b(G+)baFb.

Verification

1.1A1A2algebra

Direct multiplication gives σj2=I, Tr⁡(σj)=0, σxσy=iσz, σyσz=iσx, and σzσx=iσy; reversing the order negates each mixed product. Thus Tr⁡(σjσk)=2δjk. Since a Hermitian 2×2 matrix has two real diagonal entries and one complex off-diagonal entry, dim⁡RH2=4; the four nonzero orthogonal matrices I,σx,σy,σz form a basis. Write ρ=aI+∑jbjσj in this basis. Since Tr⁡(I)=2 and Tr⁡(σj)=0, the trace-one condition gives a=1/2; pairing with σk gives Tr⁡(ρσk)=2bk. Thus the representation is unique with rk:=Tr⁡(ρσk)=2bk∈R. Write r=(rx,ry,rz)∈R3 and ∥r∥2=(rx2+ry2+rz2)1/2. Positivity [A2] restricts ∥r∥2≤1, since (rxσx+ryσy+rzσz)2=∥r∥22I and the eigenvalues of ρ are (1±∥r∥2)/2.

2.1A1step 1.1algebra

Each σj is Hermitian with eigenvalues 1,−1 by step 1.1, so Fj,± has eigenvalues 1/3,0 and is positive. For each j, Fj,++Fj,−=I/3; summing over x,y,z gives I. Also Fj,+−Fj,−=σj/3, so each σj lies in the real span of the effects. Since I is their sum, the basis in step 1.1 shows that the effects span H2.

3.1A1step 1.1step 2.1algebra

In the coordinate order (x+,x−,y+,y−,z+,z−), direct multiplication and step 1.1 give G(j,s),(k,t)=136Tr⁡((I+sσj)(I+tσk))=118(1+st δjk) for s,t∈{+1,−1}. Thus the diagonal entries are 1/9, opposite outcomes on one axis have entry 0, and distinct axes have entry 1/18. Let u=(1,1,1,1,1,1) and vj=ej+−ej−. Then Gu=u/3, Gvj=vj/9, and G vanishes on vectors (sx,sx,sy,sy,sz,sz) with sx+sy+sz=0. These mutually orthogonal subspaces have dimensions 1,3,2 and span R6.

4.1A1A2F1F2F3step 1.1step 2.1step 3.1algebra

By [F2] and the eigenspaces in step 3.1, G+=3Pu+9∑jPvj, where Pu=uuT/6 and Pvj=vjvjT/2. The ej+ column of G+ is u/2+9vj/2. Applying the canonical-dual formula [F3], using ∑k,±Fk,±=I and Fj,+−Fj,−=σj/3, gives Dj,+=12I+32σj; similarly Dj,−=12I−32σj. The probabilities are pj,±=Tr⁡(ρFj,±)=(1±rj)/6, so ∑j,±pj,±Dj,±=12I+12∑jrjσj=ρ, in agreement with [F1].

5.1A1step 4.1algebra∎

The frequency vector with fx,+=1 and all other entries zero is nonnegative and sums to one. Its output is Dx,+=12I+32σx, whose eigenvalues are 2 and −1. It is Hermitian but not positive, so the linear-inversion procedure does not preserve positivity for every frequency vector.

Depends on

Dependency tree · 2 levels

Sources