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A New Logical Measure for Quantum Information Cover
By:   
Open Access
|Feb 2025

Figures & Tables

Table 1.

Elements-distinctions duality between the two dual logics.

Logic ℘(U) of subsets of ULogic of partitions Π(U) on U
Its/DitsElements of subsetsDistinctions of partitions
P.O.Inclusion of subsetsInclusion of ditsets
JoinUnion of subsetsUnion of ditsets
MeetSubset of common elementsDitset of common dits
Impl.S ⊃ T = U iff S ⊆ Tσ ⇒ π = 1U iff σ ≾ π
TopSubset U with all elementsPartition 1U with all distinctions
BottomSubset ∅ with no elementsPartition 0U with no distinctions
Figure 1.

The lattices of the dual subsets and partitions.

Table 2.

Duality of quantitative subsets and partitions.

Logical Probability TheoryLogical Information Theory
OutcomesElements of SDistinctions of π
EventsSubsets S ⊆ UDitsets dit(π) ⊆ U × U
pi=1n Pr⁡(S)=|S|[U] h(π)=|dit(π)||U×U|
Probs. pPr(S) = ∑ui∈S pih(π) = ∑(ui, uk)∈dit(π) pipk
Interpretation1-draw prob. of S-element2-draw prob. of π-distinction
Figure 2.

Venn diagram for compound logical entropies.

Table 3.

The dit-bit transform from logical entropy to Shannon entropy.

The Dit-Bit Transform: 1−pi⇝log⁡(1pi)
h(p) =∑ipi(1 − pi)
H(p) =∑ipi log(1/pi)
h(X, Y) =∑x,y p (x, y) [1 − p(x, y)]
H(X, Y) = ∑x,yp(x,y)log⁡(1p(x,y))
h(X|Y) =∑x,y p (x, y) [(1 − p(x, y) − (1 − p(y))]
H(X|Y) = ∑x,yp(x,y)[log⁡(1p(x,y))−log⁡(1p(y))]
m(X, Y)∑x,y p(x,y) [[1 − p(x)] + [1 − p(y)] − [1 − p(x,y)]]
I(X, Y) ∑x,yp(x,y)[log⁡(1p(x))+log⁡(1p(y))−log⁡(1p(x,y))]
Figure 3.

Venn diagram “mnemonic” for compound Shannon entropies.

Table 4.

Dictionary translating set partitions into density matrices.

Set concept with probabilitiesSet level density matrix concept
Partition π with point probs. pDensity matrix ρ(π)=∑j=1mPr⁡(Bj)|bj〉〈bj|
Point probabilities {p1, ..., pn}Value of diagonal entries of ρ(π)
Trivial indits (ui, ui) of πDiagonal entries of ρ(π)
Non-trivial indits of πNon-zero off-diagonal entries of ρ(π)
Dits of πZero entries of ρ(π)
Sum Pr(Bj) = ∑ui∈ Bj piTrace tr[PBjρ(π)]
Block probabilities Pr(Bj) in πEigenvalues ≠ 0 of ρ(π)
Block prob. 1 of U in 0U = {U}Non-zero eigenvalue of 1 for ρ(0U)
Table 5.

Linearization dictionary to translate set concepts into corresponding vector space concepts.

Set conceptVector-space concept
Subset S ⊆ USubspace [S] ⊆ V
Partition {f−1(r)}r∈f(U)DSD {Vr}r∈f(U)
Disjoint union U = ⊎r∈f(U) f−1(r)Direct sum V = ⊕r∈f(U)Vr
Numerical attribute f : U → ℝObservable F ui = f(ui)ui
f ↾ S = rSF ui = rui
Constant set S of fEigenvector ui of F
Value r on constant set SEigenvalue r of eigenvector ui
Characteristic fcn. χS : U → {0, 1}Projection operator P[S]ui = χS (ui)ui
∑r∈f(U) χf−1 (r) = χU∑r∈f(U) Pr = I : V → V
Spectral Decomp. f = ∑r∈f(U) rχf−1(r)Spectral Decomp. F = ∑r∈f(U) rPr
Set of r-constant sets ℘(f−1(r))Eigenspace Vr of r-eigenvectors
Direct product U × UTensor product V ⊗ V
Table 6.

Yoga of Linearization without probabilities case.

Logical entropyQuantum logical entropy
U = {u1, ..., un}ON basis U for Hilbert space V
f, g :U → ℝCommuting F , G : V → V
{r}r∈f(U), {S}s∈g(U)Eigenvalues of F and G
π = {f−1(r)}r∈f(U), σ = {g−1(S)}s∈g(U)DSDs of eigenspaces of F , G
Dits of π : (ui, uk), f(ui) ≠ f(uk)Qudits F : ui ⊗ uk, f(ui) ≠ f(uk)
Dits of σ : (ui, uk), g(ui) ≠ g(uk)Qudits G: ui ⊗ uk, g(ui) ≠ g(uk)
Ditset of π: dit(π)[qudit(F)]: Subspace generated in V ⊗ V
Ditset of σ: dit(σ)[qudit(G)]: Subspace generated in V ⊗ V
Join: dit(π) ∪ dit(σ) ⊆ U × U[qudit(F) ∪ qudit(G)] ⊆ V ⊗ V
Difference: dit(π) − dit(σ) ⊆ U × U[qudit(F) − qudit(G)] ⊆ V ⊗ V
Mutual: dit(π) ∩ dit(σ) ⊆ U × U[qudit(F) ∩ qudit(G)] ⊆ V ⊗ V
Table 7.

Logical entropy + Linearization = quantum logical entropy.

Logical entropyQuantum logical entropy
ρ(0U) = ρ(U) = ρ(U)2Pure state ρ(ψ) = ρ(ψ)2
p × p on U × Uρ(ψ) ⊗ ρ(ψ) on V ⊗ V
h(0U) = 1 − tr[ρ(0U)2] = 0h(ρ(ψ)) = 1 − tr[ρ(ψ)2] = 0
π = f−1, h(π) = p × p(dit(π))h(F : ψ) = tr[P[qudit(F)]ρ (ψ) ⊗ ρ(ψ)]
h(π, σ) = p × p(dit(π) ∪ dit(σ))tr [P[qudit(F)∪qudit(G)]ρ(ψ) ⊗ ρ(ψ)]
h(π|σ) = p × p(dit(π) − dit(σ))tr [P[qudit(F)−qudit(G)]ρ(ψ) ⊗ ρ(ψ)]
m(π σ) = p × p(dit(π) ∩ dit(σ))tr [P[qudit(F)∩qudit(G)]ρ(ψ) ⊗ ρ(ψ)]
h (π) = h (π|σ) + m(π, σ)h(F : ψ) = h(F|G : ψ) + m(F, G : ψ)
ρ(π) = ρ̂(0U) = ∑r∈f(U) Prρ(0U)P rρ̂(ψ) = ∑r∈f(U) Prρ(ψ)Pr
h(π) = 1 − tr [ρ(π)2]h(F : ψ) = 1 − tr[ρ̂(ψ)2]
Figure 4.

Venn diagram relationships for quantum logical entropy.

DOI: https://doi.org/10.2478/qic-2025-0005 | Journal eISSN: 3106-0544 (formerly 1533-7146) | Journal ISSN: 1533-7146
Language: English
Page range: 82 - 96
Submitted on: Dec 20, 2024
Accepted on: Feb 11, 2025
Published on: Feb 25, 2025
Published by: Cerebration Science Publishing Co., Limited
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 David Ellerman, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.