copy-cat strategies and information flow in physics ... · logic from proof nets to diagram...
TRANSCRIPT
![Page 1: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/1.jpg)
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 1 / 67
Copy-Cat Strategies and Information Flow in Physics, Geometry, Logicand Computation
Samson Abramsky
Oxford University Computing Laboratory
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Introduction
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 2 / 67
![Page 3: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/3.jpg)
How to Beat a Grand-Master
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 3 / 67
![Page 4: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/4.jpg)
How to Beat a Grand-Master
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 3 / 67
Kasparov Short
The Copy-Cat Strategy
![Page 5: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/5.jpg)
Does Copy-Cat still work here?
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 4 / 67
Kasparov Short Short
B
W
W
B
W
B
·
OOOOOOOOOOOO
ooooooooooooo
fffffffffffffffffffffffffffff
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Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
![Page 7: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/7.jpg)
Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
![Page 8: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/8.jpg)
Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
• Correspondence between interactive and geometric views of
the same phenomena
![Page 9: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/9.jpg)
Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
• Correspondence between interactive and geometric views of
the same phenomena
• Emergence
![Page 10: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/10.jpg)
Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
• Correspondence between interactive and geometric views of
the same phenomena
• Emergence
• Copy-cat vs. Cloning:
![Page 11: Copy-Cat Strategies and Information Flow in Physics ... · Logic From Proof Nets to Diagram Algebras Temperley-Lieb Algebra Characterizing Planarity Composition Functional Computation:](https://reader035.vdocuments.site/reader035/viewer/2022071215/604503a13b06f154a85740c3/html5/thumbnails/11.jpg)
Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
• Correspondence between interactive and geometric views of
the same phenomena
• Emergence
• Copy-cat vs. Cloning:
◦ Linear vs. Classical Logic
◦ Quantum vs. Classical Physics
◦ Linear-time vs. Computationally universal
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Some themes
Introduction• How to Beat aGrand-Master• Does Copy-Cat stillwork here?
• Some themes
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 5 / 67
• Common fundamental structures of interaction: logical,
computational, physical, geometric
• Correspondence between interactive and geometric views of
the same phenomena
• Emergence
• Copy-cat vs. Cloning:
◦ Linear vs. Classical Logic
◦ Quantum vs. Classical Physics
◦ Linear-time vs. Computationally universal
• Many pictures — serious mathematical structure underneath!
(Joyal, Street, Kelly, Penrose, . . . )
• Game aspect not emphasized — more at primitive interaction
level (‘GoI’).
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Logic
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 6 / 67
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Multiplicative Proof Structures
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 7 / 67
α⊥Oα⊥
α⊥ α⊥
α⊗ α
α α
α⊥Oα⊥
α⊥ α⊥
α⊗ α
α α
The essential information in a (cut-free) proof in MLL is the axiom links.
Accordingly, we define a proof structure on a sequent Γ to be a fixpoint-free
involution f (so f2 = 1 and f(a) 6= a) on its occurrences of literals such that if
f(a) = b, l(a) = l(b)⊥.
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From Proof Nets to Semantics
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 8 / 67
Note that proof structures as we have defined them are simply
certain permutations acting on finite sets (of literals). This leads to
the following compositional interpretation of formulas by finite sets,
and of proofs by permutations on these sets.
• A literal is interpreted by a one-point set; Tensor and Par by
disjoint union. A sequent is treated like the Par of its formulas.
Thus the set |Γ| associated to a sequent is in bijection with its set
of occurrences of literals.
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Assignment of Permutations to Sequent Proofs
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 9 / 67
Axiom
⊢ a, a⊥Id
Multiplicatives
⊢ Γ, A ⊢ ∆, B
⊢ Γ,∆, A⊗B⊗
Γ, A,B
⊢ Γ, AOBO
• Axiom: assign the transposition a↔ a⊥
• Tensor: assign the disjoint union of the two permutations
• Par: assign the same permutation!
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MLL Proof Nets
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 10 / 67
Which proof structures really come from proofs in MLL?
Switching Graphs: A switching S of Γ assigns L or R to each occurrence ofO. Given a sequent Γ, a proof structure f , and a switching S, the switchinggraph G(Γ, f, S) has:
• subformula occurrences in Γ as vertices;
• an edge connecting A to A⊗B and an edge connecting B to A⊗B for
each occurrence of A⊗B;
• an edge connecting A to AOB if S assigns L to AOB, and an edgeconnecting B to AOB if S assigns R to AOB;
• an edge connecting literal occurrences a and b if f(a) = b.
The Danos-Regnier criterion: A proof-structure f for Γ is an MLL proof-net if
for every switching S, G(Γ, f, S) is acyclic and connected.
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Results on Proof Nets
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 11 / 67
Every sequent proof in MLL canonically maps to a proof structure.
Proposition 1 (Soundness) The proof structures arising from
sequent proofs are proof nets.
Theorem 2 (Sequentialization Theorem) Every proof net arises
from a sequent proof.
This is the Geometric Criterion.
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Understanding Proof Nets Interactively
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 12 / 67
We can think of a proof structure (set of axiom links) as a copy-catstrategy, and a switching as a counter-strategy. A proof structure
will be a proof-net if its interaction with every counter-strategy yields
a correct result.
Hence we define (Girard 1988):
f⊥g ≡ fg is cyclic
i.e. (fg)k = 1 where k is the cardinality of the underlying set (of
literal occurrences), and this is true for no smaller value of k.
This condition is directly inspired by the long trip condition, the
earlier version of the proof net correctness condition used by (Girard
1987).
We can then define
S⊥ = {g | ∀f ∈ S. f⊥g}.
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Semantics of MLL Proofs
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 13 / 67
We now give a semantics of MLL proofs by specifying, for each
formula A, a set S of permutations on the set of literal occurrences
|A|, such that S = S⊥⊥.
For a literal, we specify the unique permutation (the identity).
S(A⊗B) = {f + g | f ∈ S(A) ∧ g ∈ S(B)}⊥⊥
S(AOB) = S(A⊥ ⊗B⊥)⊥.
Note that, for every formula A: S(A⊥) = S(A)⊥.
We extend this assignment to sequents Γ by treating Γ as the Par of
its formulas.
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Semantics: Soundness and Completeness
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 14 / 67
Proposition 3 (Semantic Soundness) If f is the permutation
assigned to a sequent proof of Γ, then f ∈ S(Γ).
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Semantics: Soundness and Completeness
Introduction
Logic
• Multiplicative ProofStructures• From Proof Nets toSemantics• Assignment ofPermutations toSequent Proofs
• MLL Proof Nets
• Results on Proof Nets• Understanding ProofNets Interactively
• Semantics of MLLProofs• Semantics:Soundness andCompleteness
• Cut-Elimination byPermutations• Cut-Elimination byPermutations Ctd
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 14 / 67
Proposition 5 (Semantic Soundness) If f is the permutation
assigned to a sequent proof of Γ, then f ∈ S(Γ).
Theorem 6 (Full Completeness) If f ∈ S(Γ) is a
literal-respecting involution, then f is a proof-net, and hence is the
denotation of a sequent proof.
This shows the equivalence of the geometric and interactivecriteria for proofs.
Proof Outline: Given σ ∈ S(Γ), we assume that for some switching
S, G(Γ, σ, S) is not a tree. Then we construct a counter-strategyτ ∈ S(Γ)⊥ such that ¬(σ⊥τ). Contradiction.
Discussion: Non-emptiness of S(A), “paraproofs”, and uniformity.
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Cut-Elimination by Permutations
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 15 / 67
We consider performing Cut-elimination on twist ◦ twist: The proof net for
twist ◦ twist before cut elimination is:
@@
@��
� @@
@��
�@@
@��
�@@
@��
�
α⊥Oα⊥ α⊗ α α⊥
Oα⊥ α⊗ α
α⊥ α⊥ α α α⊥ α⊥α α
The proof net for twist ◦ twist after one step of Cut elimination is:
@@@�
�� @@@�
��ααα⊥α⊥ααα⊥α⊥
α⊗ ααOα
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Cut-Elimination by Permutations Ctd
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 16 / 67
Generally, in this fragment, we can apply this ‘decomposition rule’ repeatedly
for tensors cut against par until all cuts are between axiom links. We can saythat the whole purpose of these transformations is to match up the
corresponding axiom links correctly; the ‘real’ information flow is then
accomplished by the axiom reductions:
-α⊥ α α⊥ α α⊥ α
or more generally,
-α⊥ α α⊥ α α⊥ α α⊥ α
Two views: geometric and interactive.
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From Proof Nets to DiagramAlgebras
Introduction
Logic
From Proof Nets toDiagram Algebras
• From Proof Nets toDiagram Algebras
• Diagrams for Arrows
• Example
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 17 / 67
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From Proof Nets to Diagram Algebras
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 18 / 67
We now make a transition from an apparently very specialized corner of Proof
Theory to a broad topic arising in Representation Theory, Knot Theory, andwith connections to Mathematical Physics.
We shall on the one hand lose some structure, and on the other gain some.
• We shall obliterate the distinction between ⊗ and O; this corresponds to
moving from ∗-autonomous to compact closed categories. This means
that we can forget about the formula tree structure altogether; we are simply
connecting up literal occurrences, which we shall draw as “joining up thedots”.
Motivation: compact closed categories show up in many contexts of interest!
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Diagrams for Arrows
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 19 / 67
• On the other hand, rather than one-sided sequents, we shall represent
general arrows or two-sided sequents diagrammatically. This means werepresent arrows
A1 ⊗ · · · ⊗An −→ B1 ⊗ · · · ⊗Bm
where each Ai and Bj is a literal. We represent such arrows by
literal-preserving involutions on {1, . . . , n}+ {1, . . . ,m}, where
literal-preserving now means:
◦ We connect opposite literals in the domain or codomain, or
◦ We connect occurrences of the same literal in the domain and thecodomain.
An advantage of this representation is that we express composition very
transparently, by “stacking” arrows.
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Example
Introduction
Logic
From Proof Nets toDiagram Algebras
• From Proof Nets toDiagram Algebras
• Diagrams for Arrows
• Example
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 20 / 67
The composition of
a
a
b b∗
a∗ a
and
a
a
a∗ a
c∗ c
is given by
a
a
b b∗
c∗ c
a a∗ a =
a
a
b b∗
c∗ c
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Temperley-Lieb Algebra
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
• Temperley-LiebAlgebra
• TL algebra:generators and relations
• Temperley-LiebMonoids• Diagram Monoids:Generators• Diagram Monoids:Relations• Expressiveness of theGenerators• Nested Cups andCaps
• The Trace• Trace of Identity is theDimension• The Connection toKnots
• An Algebraic View
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 21 / 67
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Temperley-Lieb Algebra
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
• Temperley-LiebAlgebra
• TL algebra:generators and relations
• Temperley-LiebMonoids• Diagram Monoids:Generators• Diagram Monoids:Relations• Expressiveness of theGenerators• Nested Cups andCaps
• The Trace• Trace of Identity is theDimension• The Connection toKnots
• An Algebraic View
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 22 / 67
The Temperley-Lieb algebra played a central role in the Jonespolynomial invariant of knots and ensuing developments.
The TL algebra was originally presented, rather forbiddingly, in terms
of abstract generators and relations. It was recast in beautifullyelementary and conceptual terms by Louis Kauffman as a planardiagram algebra.
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TL algebra: generators and relations
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 23 / 67
We fix a ring R. Given a choice of parameter τ ∈ R and a dimension n ∈ N,
we define the Temperley-Lieb algebraAn(τ) to be the unital, associativeR-linear algebra with generators
U1, . . . , Un−1
and relationsUiUjUi = Ui |i− j| = 1
U2i = τ · Ui
UiUj = UjUi |i− j| > 1
Note that the only relations used in defining the algebra are multiplicative ones.
This suggests that we can present the multiplicative monoidMn, and thenobtainingAn(τ) as the monoid algebra of formal R-linear combinations∑
i ri · ai overMn, with multiplication defined by bilinear extension:
(∑
i
ri · ai)(∑
j
sj · bj) =∑
i,j
(risj) · (aibj).
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Temperley-Lieb Monoids
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
• Temperley-LiebAlgebra
• TL algebra:generators and relations
• Temperley-LiebMonoids• Diagram Monoids:Generators• Diagram Monoids:Relations• Expressiveness of theGenerators• Nested Cups andCaps
• The Trace• Trace of Identity is theDimension• The Connection toKnots
• An Algebraic View
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 24 / 67
We defineMn as the monoid with generators
δ, U1, . . . , Un−1
and relations
UiUjUi = Ui |i− j| = 1U2
i = δUi
UiUj = UjUi |i− j| > 1δUi = Uiδ
We can then obtainAn(τ) as the monoid algebra overMn, subject
to the identification
δ = τ · 1.
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Diagram Monoids: Generators
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 25 / 67
We start with two parallel rows of n dots (geometrically, points in the plane). An
element of the monoid is obtained by “joining up the dots” pairwise in a smooth,planar fashion, where the arc connecting each pair of dots must lie within the
rectangle framing the two parallel rows of dots. Such diagrams are identified up
to planar isotopy, i.e. continuous deformation within the portion of the plane
bounded by the framing rectangle..
The generators U1, . . . , Un−1 can be drawn as follows:
· · ·
· · ·
1 2 3 n
1 2 3 n
U1
· · ·
· · ·
· · ·
1 n
1 n
Un−1
The generator δ corresponds to a loop©— all such loops are identified up to
isotopy.
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Diagram Monoids: Relations
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 26 / 67
We refer to arcs connecting dots in the top row as cups, those connecting dots
in the bottom row as caps, and those connecting a dot in the top row to a dot inthe bottom row as through lines.
Multiplication xy is defined by identifying the bottom row of x with the top row
of y, and composing paths. In general loops may be formed — these are
“scalars”, which can float freely across these figures. The relations can be
illustrated as follows:
=
U1U2U1 = U1
=
U21 = δU1
=
U1U3 = U3U1
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Expressiveness of the Generators
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 27 / 67
The fact that all planar diagrams can be expressed as products of generators is
not entirely obvious. As an illustrative example, consider the planar diagrams inM3. Apart from the generators U1, U2, and ignoring loops, there are three:
The first is the identity for the monoid; we refer to the other two as the left waveand right wave respectively. The left wave can be expressed as the productU2U1:
=
The right wave has a similar expression.
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Nested Cups and Caps
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 28 / 67
Once we are in dimension four or higher, we can have nested cups and caps.
These can be built using waves, as illustrated by the following:
=
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The Trace
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 29 / 67
There is a natural trace function on the Temperley-Lieb algebra, which can bedefined diagrammatically onMn by connecting each dot in the top row to the
corresponding dot in the bottom row, using auxiliary cups and cups. This
always yields a diagram isotopic to a number of loops — hence to a scalar, as
expected. This trace can then be extended linearly to An(τ).
We illustrate this firstly by taking the trace of a wave—which is equal to a single
loop:
=
The Ear is a Circle
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Trace of Identity is the Dimension
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 30 / 67
Our second example illustrates the important general point that the trace ofthe identity inMn is δn:
=
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The Connection to Knots
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 31 / 67
How does this connect to knots? Again, a key conceptual insight is due to
Kauffman, who saw how to recast the Jones polynomial in elementary
combinatorial form in terms of his bracket polynomial. The basic idea of the
bracket polynomial is expressed by the following equation:
= +A B
Each over-crossing in a knot or link is evaluated to a weighted sum of the two
possible planar smoothings. With suitable choices for the coefficients A and B
(as Laurent polynomials), this is invariant under the second and third
Reidemeister moves. With an ingenious choice of normalizing factor, itbecomes invariant under the first Reidemeister move — and yields the Jones
polynomial!
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An Algebraic View
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 32 / 67
What this means algebraically is that the braid group Bn has a representation
in the Temperley-Lieb algebraAn(τ) — the above bracket evaluation showshow the generators βi of the braid group are mapped into the Temperley-Lieb
algebra:
βi 7→ A · Ui +B · 1.
Every knot arises as the closure (i.e. the diagrammatic trace) of a braid; the
invariant arises by mapping the open braid into the Temperley-Lieb algebra,and taking the trace there.
This is just the beginning of a huge swathe of further developments, including:
Topological Quantum Field Theories, Quantum Groups, Quantum Statistical
mechanics, Diagram Algebras and Representation Theory, and more.
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Characterizing Planarity
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
• CharacterizingPlanarity
• First condition
• Second condition
• Planarity for Points
• Names and Conames
• Example
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 33 / 67
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Characterizing Planarity
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
• CharacterizingPlanarity
• First condition
• Second condition
• Planarity for Points
• Names and Conames
• Example
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 34 / 67
A map f ∈ Inv(N(n,m)) will be called planar if it satisfies the
following two conditions, for all i, j ∈ N(n,m):
(PL1) i < j < f(i) =⇒ f(j) < f(i)(PL2) f(i) # i < j # f(j) =⇒ f(i) < f(j).
It is instructive to see which possibilities are excluded by these
conditions.
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First condition
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
• CharacterizingPlanarity
• First condition
• Second condition
• Planarity for Points
• Names and Conames
• Example
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 35 / 67
(PL1) i < j < f(i) =⇒ f(j) < f(i)
(PL1) rules out
· · ·
· · ·
i j f(i)
f(j)
where f(j) # f(i), and also
i j f(i)f(j)
where f(i) < f(j).
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Second condition
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
• CharacterizingPlanarity
• First condition
• Second condition
• Planarity for Points
• Names and Conames
• Example
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 36 / 67
(PL2) f(i) # i < j # f(j) =⇒ f(i) < f(j).
Similarly, (PL2) rules out
· · · · · ·
· · ·
i j
f(j) f(i)
We write P(n,m) for the set of planar maps in Inv(N(n,m)).
Proposition 7
1. Every planar diagram satisfies the two conditions.
2. Every involution satisfying the two conditions can be drawn as a
planar diagram.
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Planarity for Points
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 37 / 67
Rather than proving this directly, it is simpler, and also instructive, to reduce it to
a special case. We consider arrows in D of the special form I → n. Such
arrows consist only of caps. They correspond to points, or states in the
terminology of Categorical QM.
Since the top row of dots is empty, in this case we have a linear order, and the
premise of condition (PL2) can never arise. Hence planarity for such arrows isjust the simple condition (PL1) — which can be seen to be equivalent to saying
that, if we write a left parenthesis for each left end of a cap, and a right
parenthesis for each right end, we get a well-formed string of parentheses.
Thus
corresponds to
()(()).
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Names and Conames
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 38 / 67
Now we recall that quite generally, in any pivotal category we have the
Hom-Tensor adjunction
A⊗B∗ ⌊f⌋- I
≃←→ A
f- B
≃←→ I
⌈f⌉- A∗⊗B
pfq = (1A∗⊗f)◦ηA : I → A∗⊗B xfy = ǫB ◦(f⊗1B∗) : A⊗B∗ → I.
We call pfq the name of f , and xfy the coname. The inverse to the map
f 7→ pfq is defined by
g : I → A∗ ⊗B 7→ (ǫA ⊗ 1B) ◦ (1A ⊗ g) : A→ B.
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Example
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 39 / 67
We compute the name of the left wave:
=
Applying the inverse transformation:
=
Note also that the unit is the name of the identity: ηn = p1nq, and similarly
ǫn = x1ny.
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Composition
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
• The Temperley-LiebCategory
• Composition• The ‘ExecutionFormula’• Reading theExecution Formula• Reading theExecution Formula
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 40 / 67
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The Temperley-Lieb Category
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
• The Temperley-LiebCategory
• Composition• The ‘ExecutionFormula’• Reading theExecution Formula• Reading theExecution Formula
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 41 / 67
Our aim is now to define a category T , which will yield the desired
description of the Temperley-Lieb monoids. The objects of T are
the natural numbers. The homset T (n,m) is defined to be the
cartesian product N× P(n,m). Thus a morphism n→ m in Tconsists of a pair (s, f), where s is a natural number, andf ∈ P(n,m) is a planar map in Inv(N(n,m)).
It remains to define the composition and identities in this category.
Clearly (even leaving aside the natural number components of
morphisms) composition cannot be defined as ordinary function
composition. This does not even make sense — the codomain of amorphism f : n→ m does not match the domain of a morphism
g : m→ p — let alone yield a function with the necessary
properties to be a morphism in the category.
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Composition
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
• The Temperley-LiebCategory
• Composition• The ‘ExecutionFormula’• Reading theExecution Formula• Reading theExecution Formula
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 42 / 67
Consider a map f : [n] + [m] −→ [n] + [m]. Each input lies in
either [n] or [m] (exclusive or), and similarly for the corresponding
output. This leads to a decomposition of f into four disjoint partialmaps:
fn,n : [n] −→ [n] fn,m : [n] −→ [m]fm,n : [m] −→ [n] fm,m : [m] −→ [m]
so that f can be recovered as the disjoint union of these four maps.
If f is an involution, then these maps will be partial involutions.
Now suppose we have maps f : [n] + [m]→ [n] + [m] and
g : [m] + [p]→ [m] + [p]. We write the decompositions of f and gas above in matrix form:
f =
(
fn,n fn,m
fm,n fm,m
)
g =
(
gm,m gm,p
gp,m gp,p
)
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The ‘Execution Formula’
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 43 / 67
We can view these maps as binary relations on [n] + [m] and [m] + [p]respectively, and use relational algebra (union R ∪ S, relational composition
R;S and reflexive transitive closure R∗) to define a new relation θ on[n] + [p]. If we write
θ =
(
θn,n θn,p
θp,n θp,p
)
so that θ is the disjoint union of these four components, then we can define it
component-wise as follows:
θn,n = fn,n ∪ fn,m; gm,m; (fm,m; gm,m)∗; fm,n
θn,p = fn,m; (gm,m; fm,m)∗; gm,p
θp,n = gp,m; (fm,m; gm,m)∗; fm,n
θp,p = gp,p ∪ gp,m; fm,m; (gm,m; fm,m)∗; gm,p.
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Reading the Execution Formula
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 44 / 67
We can give clear intuitive readings for how these formulas express
composition of paths in diagrams in terms of relational algebra:
• The component θn,n describes the cups of the diagram resulting from the
composition. These are the union of the cups of f (fn,n), together with
paths that start from the top row with a through line of f , given by fn,m,
then go through an alternating odd-length sequence of cups of g (gm,m)
and caps of f (fm,m), and finally return to the top row by a through line of f
(fm,n).
· · ·
fn,m; gm,m; (fm,m; gm,m)∗; fm,n
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Reading the Execution Formula
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 45 / 67
• Similarly, θp,p describes the caps of the composition.
• θn,p = θcp,n describe the through lines. Thus θn,p describes paths which
start with a through line of f from n to m, continue with an alternating
even-length (and possibly empty) sequence of cups of g and caps of f , and
finish with a through line of g from m to p.
· · ·
fn,m; (gm,m; fm,m)∗; gm,p
All through lines from n to p must have this form.
Proposition 8 If f and g are planar, so is θ.
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Functional Computation: ThePlanar λ-calculus
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
• An Example
• Example ctd
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 46 / 67
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An Example
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 47 / 67
We shall consider the bracketing combinator
B ≡ λx.λy.λz. x(yz) : (B → C)→ (A→ B)→ (A→ C).
This is characterized by the equation Babc = a(bc).
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An Example
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 47 / 67
We shall consider the bracketing combinator
B ≡ λx.λy.λz. x(yz) : (B → C)→ (A→ B)→ (A→ C).
This is characterized by the equation Babc = a(bc).
We take A = B = C = 1 in TL. The interpretation of the open term
x : B → C, y : A→ B, z : A ⊢ x(yz) : C
is as follows:x+ x− y+ y− z+
o
Here x+ is the output of x, and x− the input, and similarly for y. The output of
the whole expression is o.
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Example ctd
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 48 / 67
When we abstract the variables, we obtain the following caps-only diagram:
x+x−y+y−z+o
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Example ctd
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 48 / 67
When we abstract the variables, we obtain the following caps-only diagram:
x+x−y+y−z+o
Now we consider an application Babc:
x+x−y+y−z+o
a b c a b c
o
=
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Functional Computation:Non-Planar Combinators
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
• An Example
• Example ctd
• Wider perspective
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 49 / 67
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An Example
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 50 / 67
We shall consider the commuting combinator
C ≡ λx.λy.λz. xzy : (A→ B → C)→ B → A→ C.
This is characterized by the equation Cabc = acb.
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An Example
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 50 / 67
We shall consider the commuting combinator
C ≡ λx.λy.λz. xzy : (A→ B → C)→ B → A→ C.
This is characterized by the equation Cabc = acb.
The interpretation of the open term
x : A→ B → C, y : B, z : A ⊢ xzy : C
is as follows:x+ x1 x2 y z
o
Here x+ is the output of x, x1 the first input, and x2 the second input. The
output of the whole expression is o.
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Example ctd
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 51 / 67
When we abstract the variables, we obtain the following caps-only diagram:
x+x1x2yzo
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Example ctd
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 51 / 67
When we abstract the variables, we obtain the following caps-only diagram:
x+x1x2yzo
Now we consider an application Cabc:
x+x1x2yzo
a b c a b c
o
=
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Wider perspective
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
• An Example
• Example ctd
• Wider perspective
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 52 / 67
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Wider perspective
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
• An Example
• Example ctd
• Wider perspective
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 52 / 67
• The Brauer algebra (1931) arises if we drop the planarity
condition on the TL algebra. This plays an important role in the
representation theory of the Orthogonal group (‘Schur-Weyl
duality’). A whole genre of ‘diagram algebras’ in Representation
Theory.
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Wider perspective
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
• An Example
• Example ctd
• Wider perspective
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 52 / 67
• The Brauer algebra (1931) arises if we drop the planarity
condition on the TL algebra. This plays an important role in the
representation theory of the Orthogonal group (‘Schur-Weyl
duality’). A whole genre of ‘diagram algebras’ in Representation
Theory.
• With BCI combinators one can interpret Linear λ-calculus.
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Wider perspective
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
• An Example
• Example ctd
• Wider perspective
Logic of QuantumInformation Flow
Cloning
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 52 / 67
• The Brauer algebra (1931) arises if we drop the planarity
condition on the TL algebra. This plays an important role in the
representation theory of the Orthogonal group (‘Schur-Weyl
duality’). A whole genre of ‘diagram algebras’ in Representation
Theory.
• With BCI combinators one can interpret Linear λ-calculus.
• One can retrieve the Kelly-Laplaza construction of the free
compact closed category by a straightforward generalization.
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Logic of Quantum InformationFlow
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 53 / 67
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Quantum Entanglement
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 54 / 67
Bell state:|00〉+ |11〉
EPR state:|01〉+ |10〉
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Quantum Entanglement
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 54 / 67
Bell state:|00〉+ |11〉
EPR state:|01〉+ |10〉
Compound systems are represented by tensor product: H1 ⊗H2. Typical
element:∑
i
λi · φi ⊗ ψi
Superposition encodes correlation. Einstein’s ‘spooky action at a distance’.Even if the particles are spatially separated, measuring one has an effect on
the state of the other.
Bell’s theorem: QM is essentially non-local.
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From ‘paradox’ to ‘feature’: Teleportation
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 55 / 67
MBell
Ux
|00〉+ |11〉
x ∈ B2
|φ〉
|φ〉
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What is the output?
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 56 / 67
f1
f2
f3
f4
φin
φout?
(Pf4⊗1)◦(1⊗Pf3
)◦(Pf2⊗1)◦(1⊗Pf1
) : H1⊗H2⊗H3 −→ H1⊗H2⊗H3
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What is the output?
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 56 / 67
f1
f2
f3
f4
φin
φout?
(Pf4⊗1)◦(1⊗Pf3
)◦(Pf2⊗1)◦(1⊗Pf1
) : H1⊗H2⊗H3 −→ H1⊗H2⊗H3
φout = f3 ◦ f4 ◦ f†2 ◦ f
†3 ◦ f1 ◦ f2(φin)
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Follow the line!
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 57 / 67
f1
f2
f3
f4
f3 ◦ f4 ◦ f†2 ◦ f
†3 ◦ f1 ◦ f2
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Graphical Calculus for Information Flow
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 58 / 67
Compact Closure: The basic algebraic laws for units and counits.
= =
(ǫA⊗1A)◦(1A⊗ηA) = 1A (1A∗⊗ǫA)◦(ηA⊗1A∗) = 1A∗
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Projectors Decomposed
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 59 / 67
f †
f
BA∗
A∗ B
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Compositionality
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 60 / 67
The key algebraic fact from which teleportation (and many other
protocols) can be derived.
f
g
=
f
g
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Compositionality ctd
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 61 / 67
f
g
=
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Compositionality ctd
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 62 / 67
f
g
=
g
f
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Teleportation diagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 63 / 67
βi
β−1
i
=
βi
β−1
i
=
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It’s Logic!
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow• QuantumEntanglement
• From ‘paradox’ to‘feature’: Teleportation
• What is the output?
• Follow the line!• Graphical Calculus forInformation Flow• ProjectorsDecomposed
• Compositionality
• Compositionality ctd
• Compositionality ctd
• Teleportationdiagrammatically
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 64 / 67
The graphical calculus can be seen as a calculus of proofs for a
certain logic — which is highly non-classical, (in particular
resource-sensitive, so e.g. it builds in ‘No Cloning’), but also very
different from the Birkhoff-von Neumann quantum logic.
Simplification of diagrams — ‘straightening out the lines’ —corresponds to normalization or cut-elimination of proofs.
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Cloning
Introduction
Logic
From Proof Nets toDiagram Algebras
Temperley-Lieb Algebra
Characterizing Planarity
Composition
FunctionalComputation: ThePlanar λ-calculus
FunctionalComputation:Non-PlanarCombinators
Logic of QuantumInformation Flow
Cloning
• Cloning vs. Copy-cat
• Some References
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 65 / 67
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Cloning vs. Copy-cat
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 66 / 67
Copy-cat is linear copying: swapping A↔ A, rather than cloningA→ A,A.
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Cloning vs. Copy-cat
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 66 / 67
Copy-cat is linear copying: swapping A↔ A, rather than cloningA→ A,A.
• In Logic, Cloning corresponds to the Contraction rule
Γ, A,A ⊢ B
Γ, A ⊢ B
and takes us from Linear to Classical Logic.
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Cloning vs. Copy-cat
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 66 / 67
Copy-cat is linear copying: swapping A↔ A, rather than cloningA→ A,A.
• In Logic, Cloning corresponds to the Contraction rule
Γ, A,A ⊢ B
Γ, A ⊢ B
and takes us from Linear to Classical Logic.
• In Computation, Cloning allows us to define combinators such as
Wxy = xyy
and takes us from linear-time to universal computational power.
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Cloning vs. Copy-cat
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 66 / 67
Copy-cat is linear copying: swapping A↔ A, rather than cloningA→ A,A.
• In Logic, Cloning corresponds to the Contraction rule
Γ, A,A ⊢ B
Γ, A ⊢ B
and takes us from Linear to Classical Logic.
• In Computation, Cloning allows us to define combinators such as
Wxy = xyy
and takes us from linear-time to universal computational power.
• In Physics, Cloning can be used to express the passage from quantum to
classical: given the choice of a basis, we can define a linear map
H −→ H⊗H.
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Some References
Copy-Cat Strategies and Information Flow CQL Workshop August 2007 – 67 / 67
Papers available from my webpages
http://web.comlab.ox.ac.uk/oucl/work/samson.abramsky/
• Abramsky, S. and Coecke, B. (2004) A categorical semantics of quantum
protocols. Proceedings of the 19th Annual IEEE Symposium on Logic in
Computer Science (LiCS‘04), IEEE Computer Science Press.
• Abramsky, S. (2005) Abstract Scalars, Loops, and Free Traced and Strongly
Compact Closed Categories. In Proceedings of CALCO 2005, Springer
LNCS Vol. 3629, 1–31, 2005.• S. Abramsky, Temperley-Lieb algebra: From knot theory to logic and
computation via quantum mechanics. To appear in: Mathematics of
Quantum Computing and Technology, ed. Chen, Kauffman and Lomonaco.
Taylor and Francis, 2007.
• S. Abramsky, Information, Processes and Games. To appear in: Handbookof the Philosophy of Information, ed. P. Adriaans and J. van Benthem,
Elsevier.