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Noncommutative disintegration Arthur J. Parzygnat & Benjamin P. Russo University of Connecticut Farmingdale State College SUNY Category Theory OctoberFest 2018 The City College of New York (CUNY) October 28, 2018 Arthur J.


  1. Noncommutative disintegration Arthur J. Parzygnat ∗ & Benjamin P. Russo † ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberFest 2018 The City College of New York (CUNY) October 28, 2018 Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 1 / 29

  2. Outline Deterministic and nondeterministic processes 1 Stochastic matrices 2 Standard definitions The category of stochastic maps Classical disintegrations 3 Classical disintegrations: intuition Diagrammatic disintegrations Classical disintegrations exist and are unique a.e. Quantum disintegrations 4 Completely positive maps and ∗ -homomorphisms Non-commutative disintegrations Existence and uniqueness Examples Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 2 / 29

  3. � � � � � � � � � Deterministic and nondeterministic processes Category theory as a theory of processes Processes can be deterministic or non-deterministic a f b d i c j e g k h Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 3 / 29

  4. Stochastic matrices Standard definitions Stochastic maps � X assigns a Let X and Y be finite sets. A stochastic map r : Y probability measure on X to every point in Y . It is a function whose value at a point “spreads out” over the codomain. Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 4 / 29

  5. Stochastic matrices Standard definitions Stochastic maps � X assigns a Let X and Y be finite sets. A stochastic map r : Y probability measure on X to every point in Y . It is a function whose value at a point “spreads out” over the codomain. X Y r y • y Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 4 / 29

  6. Stochastic matrices Standard definitions Stochastic maps � X assigns a Let X and Y be finite sets. A stochastic map r : Y probability measure on X to every point in Y . It is a function whose value at a point “spreads out” over the codomain. X Y r y • y The value r y ( x ) of r y at x is denoted by r xy . Since r y is a probability measure, r xy ≥ 0 for all x and y . Also, � x ∈ X r xy = 1 for all y . Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 4 / 29

  7. Stochastic matrices Standard definitions Stochastic maps from functions � Y via A function f : X → Y induces a stochastic map f : X f yx := δ yf ( x ) X Y f x • • x f ( x ) where δ yy ′ is the Kronecker delta and equals 1 if and only if y = y ′ and is zero otherwise. Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 5 / 29

  8. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 6 / 29

  9. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • • • • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 6 / 29

  10. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • µ yx • • ν zy • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 7 / 29

  11. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • µ yx • • ν zy • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 8 / 29

  12. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • µ yx • • ν zy • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 9 / 29

  13. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • µ yx • • ν zy • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 10 / 29

  14. Stochastic matrices Standard definitions Composing stochastic maps � Z of µ : X � Y followed by ν : Y � Z The composition ν ◦ µ : X is defined by matrix multiplication � ( ν ◦ µ ) zx := ν zy µ yx . y ∈ Y This is completely intuitive! If we start at x and end at z , we have the possibility of passing through any intermediate step y . These “paths” have associated probabilities, which must be added. Z Y X y • µ yx • • ν zy • • • x • • z • • Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 11 / 29

  15. Stochastic matrices Standard definitions Special case: probability measures A probability measure µ on X can be viewed as a stochastic map � X from a single element set. µ : {•} Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 12 / 29

  16. Stochastic matrices Standard definitions Special case: probability measures A probability measure µ on X can be viewed as a stochastic map � X from a single element set. µ : {•} � Y is the If f : X → Y is a function, the composition f ◦ µ : {•} pushforward of µ along f . Arthur J. Parzygnat ∗ & Benjamin P. Russo † ( ∗ University of Connecticut † Farmingdale State College SUNY Category Theory OctoberF Noncommutative disintegration October 28, 2018 12 / 29

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