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QuantumInfo.Finite.CPTPMap.Unbundled

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definition

Trace Preservation Property of a Matrix Map (Tr(M(x))=Tr(x)\text{Tr}(M(x)) = \text{Tr}(x))

#IsTracePreserving

Let RR be a semiring and A,BA, B be index types. A linear matrix map M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) is said to be trace preserving if for every matrix xMatA(R)x \in \text{Mat}_A(R), the trace of its image under MM is equal to the trace of the original matrix, i.e., Tr(M(x))=Tr(x)\text{Tr}(M(x)) = \text{Tr}(x).

theorem

MM is Trace-Preserving     Tr1(J(M))=I\iff \text{Tr}_1(\mathcal{J}(M)) = I

#IsTracePreserving_iff_trace_choi

Let RR be a semiring and A,BA, B be finite types. Let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between square matrix spaces. MM is trace-preserving if and only if the partial trace over the first subsystem of its Choi matrix J(M)\mathcal{J}(M) is equal to the identity matrix, i.e., TrB(J(M))=IA\text{Tr}_B(\mathcal{J}(M)) = I_A.

theorem

Tr(M(ρ))=Tr(ρ)\text{Tr}(M(\rho)) = \text{Tr}(\rho) for a Trace-Preserving Map MM

#apply_trace

Let RR be a semiring and A,BA, B be index types. Let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between square matrix spaces. If MM is trace-preserving, then for any matrix ρMatA(R)\rho \in \text{Mat}_A(R), the trace of the image (Mρ)(M \rho) is equal to the trace of the original matrix ρ\rho, i.e., Tr(Mρ)=Tr(ρ)\text{Tr}(M \rho) = \text{Tr}(\rho).

theorem

Trace of Choi matrix of a TP map equals A|A|

#trace_choi

Let RR be a semiring and let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between square matrix spaces. If MM is trace-preserving, then the trace of its Choi matrix J(M)\mathcal{J}(M) is equal to the cardinality of the input index set AA, denoted by A|A|.

theorem

Composition of trace-preserving maps is trace-preserving

#comp

Let RR be a semiring and A,B,CA, B, C be types representing the indices of square matrices. Let M:MatA(R)RMatB(R)M: \text{Mat}_A(R) \to_R \text{Mat}_B(R) and M2:MatB(R)RMatC(R)M_2: \text{Mat}_B(R) \to_R \text{Mat}_C(R) be two matrix maps. If MM and M2M_2 are trace-preserving, then their composition M2MM_2 \circ M is also trace-preserving.

theorem

The identity matrix map is trace-preserving

#id

The identity matrix map id\text{id} on the space of square matrices MA(R)M_A(R) is trace-preserving.

theorem

x+y=1    xM1+yM2x + y = 1 \implies x M_1 + y M_2 is trace-preserving units

#unit_linear

Let RR be a semiring and A,BA, B be index types for square matrices. Let M1,M2:MatA(R)RMatB(R)M_1, M_2: \text{Mat}_A(R) \to_R \text{Mat}_B(R) be two matrix maps. If M1M_1 and M2M_2 are trace-preserving, then for any x,yRx, y \in R such that x+y=1x + y = 1, the linear combination xM1+yM2x M_1 + y M_2 is also trace-preserving.

theorem

The Kronecker product of trace-preserving maps is trace-preserving

#kron

Let RR be a commutative semiring and let A,B,C,DA, B, C, D be types representing matrix indices. Suppose M1:MatA(R)MatB(R)M_1: \text{Mat}_A(R) \to \text{Mat}_B(R) and M2:MatC(R)MatD(R)M_2: \text{Mat}_C(R) \to \text{Mat}_D(R) are two trace-preserving linear maps between square matrix spaces. Then their Kronecker product M1kmM2:MatA×C(R)MatB×D(R)M_1 \otimes_{km} M_2: \text{Mat}_{A \times C}(R) \to \text{Mat}_{B \times D}(R) is also trace-preserving.

theorem

kNkMk=I    Φ(X)=kMkXNk\sum_k N_k^* M_k = I \implies \Phi(X) = \sum_k M_k X N_k^* is Trace-Preserving

#of_kraus_isTracePreserving

Let SS be a star-ring and A,BA, B be finite index types. Given two families of matrices Mk,NkMatB×A(S)M_k, N_k \in \text{Mat}_{B \times A}(S) for kκk \in \kappa, let Φ:MatA×A(S)MatB×B(S)\Phi: \text{Mat}_{A \times A}(S) \to \text{Mat}_{B \times B}(S) be the matrix map defined by Φ(X)=kκMkXNk\Phi(X) = \sum_{k \in \kappa} M_k X N_k^*, where NkN_k^* denotes the conjugate transpose of NkN_k. If the condition kκNkMk=I\sum_{k \in \kappa} N_k^* M_k = I holds, where II is the identity matrix, then the map Φ\Phi is trace-preserving.

theorem

Submatrix Map by Index Equivalence is Trace-Preserving

#submatrix

Let RR be a semiring and e:BAe : B \simeq A be an equivalence (bijection) between index sets AA and BB. Let Φ:MatA(R)MatB(R)\Phi : \text{Mat}_A(R) \to \text{Mat}_{B}(R) be the linear map that sends a matrix MM to its submatrix defined by ee (such that Φ(M)i,j=Me(i),e(j)\Phi(M)_{i,j} = M_{e(i), e(j)} for i,jBi, j \in B). Then Φ\Phi is trace-preserving, meaning Tr(Φ(M))=Tr(M)\text{Tr}(\Phi(M)) = \text{Tr}(M) for all MMatA(R)M \in \text{Mat}_A(R).

definition

Φ(1)=1\Phi(1) = 1 for a linear matrix map Φ\Phi (Unitality)

#Unital

Let RR be a semiring and A,BA, B be index sets. For a linear matrix map Φ:MatA(R)MatB(R)\Phi: \text{Mat}_A(R) \to \text{Mat}_B(R), Φ\Phi is said to be **unital** if it maps the identity matrix in MatA(R)\text{Mat}_A(R) to the identity matrix in MatB(R)\text{Mat}_B(R), i.e., Φ(IA)=IB\Phi(I_A) = I_B.

theorem

Unital Matrix Map Sends Identity to Identity (M(1)=1M(1) = 1)

#map_1

Let RR be a semiring and A,BA, B be types indexing square matrices. Let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between matrix spaces. If MM is unital, then M(1)=1M(1) = 1, where 11 denotes the identity matrix in the respective matrix spaces.

theorem

The identity map id\text{id} is unital

#id

Let RR be a semiring and AA be a type representing matrix indices. The identity matrix map id:MatA(R)MatA(R)\text{id}: \text{Mat}_A(R) \to \dots \text{Mat}_A(R) is unital. In other words, id(1)=1\text{id}(1) = 1, where 11 denotes the identity matrix in MA(R)M_A(R).

definition

MM is Hermitian preserving

#IsHermitianPreserving

Let RR be a star-semiring and A,BA, B be types indexing square matrices. An RR-linear map M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) is said to be **Hermitian preserving** if for every Hermitian matrix xMatA(R)x \in \text{Mat}_A(R) (where x=xx = x^\dagger), its image M(x)M(x) is also a Hermitian matrix in MatB(R)\text{Mat}_B(R).

definition

MM is a positive matrix map

#IsPositive

Let RR be a star-ordered ring (typically R\mathbb{R} or C\mathbb{C}), and let AA and BB be finite sets indexing the rows and columns of square matrices. A linear matrix map M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) is said to be **positive** if for every positive semi-definite matrix xMatA(R)x \in \text{Mat}_A(R), its image M(x)MatB(R)M(x) \in \text{Mat}_B(R) is also positive semi-definite.

definition

MM is a completely positive matrix map

#IsCompletelyPositive

Let RR be a commutative semiring and A,BA, B be finite types. A linear matrix map M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) is said to be **completely positive** if for every natural number nn, the Kronecker product of MM with the identity map on n×nn \times n matrices, denoted MkmidMatn(R)M \otimes_{km} \text{id}_{\text{Mat}_n(R)}, is a positive map.

theorem

The identity matrix map is positive

#id

The identity matrix map id\text{id} on the space of square matrices MA(R)M_A(R) is a positive map. Specifically, for any square matrix XMA(R)X \in M_A(R), if XX is a positive semidefinite matrix, then its image under the identity map id(X)=X\text{id}(X) = X is also a positive semidefinite matrix.

theorem

Composition of Hermitian-preserving maps is Hermitian-preserving

#comp

Let RR be a semiring and A,B,CA, B, C be types of indices. Let M1:MatA(R)RMatB(R)M_1: \text{Mat}_A(R) \to_R \text{Mat}_B(R) and M2:MatB(R)RMatC(R)M_2: \text{Mat}_B(R) \to_R \text{Mat}_C(R) be two matrix maps. If M1M_1 and M2M_2 are both Hermitian-preserving, then their composition M2M1M_2 \circ M_1 is also Hermitian-preserving.

theorem

MM is positive     M\implies M is Hermitian-preserving

#IsHermitianPreserving

Let RR be a semiring, and let AA and BB be finite types. Let M:MatA(R)RMatB(R)M: \text{Mat}_A(R) \to_R \text{Mat}_B(R) be a linear map between spaces of square matrices. If MM is a positive map (i.e., it maps positive semidefinite matrices to positive semidefinite matrices), then MM is hermitian-preserving (i.e., it maps Hermitian matrices to Hermitian matrices).

theorem

The composition of positive matrix maps is positive

#comp

Let RR be a semiring and A,B,CA, B, C be finite types. For any two matrix maps M1:MatA(R)RMatB(R)M_1 : \text{Mat}_A(R) \to_R \text{Mat}_B(R) and M2:MatB(R)RMatC(R)M_2 : \text{Mat}_B(R) \to_R \text{Mat}_C(R), if M1M_1 and M2M_2 are both positive maps, then their composition M2M1M_2 \circ M_1 is also a positive map.

theorem

The identity matrix map id\text{id} is positive

#id

The identity matrix map id\text{id} on the space of square matrices MA(R)M_A(R) is a positive map. Specifically, for any positive semidefinite matrix XMA(R)X \in M_A(R), its image id(X)=X\text{id}(X) = X is also positive semidefinite.

theorem

The sum of two positive matrix maps is positive

#add

Let RR be a semiring and A,BA, B be finite types. For any two matrix maps M1,M2:MatA(R)RMatB(R)M_1, M_2 : \text{Mat}_A(R) \to_R \text{Mat}_B(R), if M1M_1 and M2M_2 are both positive maps, then their sum M1+M2M_1 + M_2 is also a positive map.

theorem

Nonnegative scaling of a positive matrix map is positive

#smul

Let RR be a semiring and A,BA, B be finite types. For any matrix map M:MatA(R)RMatB(R)M : \text{Mat}_A(R) \to_R \text{Mat}_B(R) and any scalar xRx \in R, if MM is a positive map and x0x \ge 0, then the scaled map xMx \cdot M is also a positive map.

theorem

If MM is completely positive, then MkmidTM \otimes_{km} \text{id}_T is positive for any finite TT

#of_Fintype

Let RR be a commutative semiring and A,BA, B be index sets for matrices. Let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between matrix spaces. If MM is completely positive, then for any finite type TT, the Kronecker product of MM with the identity map on MatT(R)\text{Mat}_T(R) (denoted MkmidTM \otimes_{km} \text{id}_T) is a positive map from MatA×T(R)\text{Mat}_{A \times T}(R) to MatB×T(R)\text{Mat}_{B \times T}(R).

theorem

Completely positive matrix maps are positive

#IsPositive

Let RR be a star-ordered ring and A,BA, B be finite types. For any linear map between matrix spaces M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R), if MM is completely positive, then MM is positive.

theorem

MatrixMap.IsCompletelyPositive.comp

#comp

[DecidableEq B] {M₁ : MatrixMap A B R} {M₂ : MatrixMap B C R} (h₁ : M₁.IsCompletelyPositive) (h₂ : M₂.IsCompletelyPositive) : IsCompletelyPositive (M₂ ∘ₗ M₁)

theorem

The identity matrix map is completely positive

#id

Let RR be a commutative semiring and AA be a finite type. The identity matrix map id:MatA(R)MatA(R)\text{id}: \text{Mat}_A(R) \to \text{Mat}_A(R) is completely positive.

theorem

The sum of two completely positive matrix maps is completely positive

#add

Let RR be a commutative semiring and A,BA, B be finite types. If M1,M2:MatA(R)MatB(R)M_1, M_2: \text{Mat}_A(R) \to \text{Mat}_B(R) are completely positive linear matrix maps, then their sum M1+M2M_1 + M_2 is also completely positive.

theorem

0x0 \le x and MM is CP imply xMx \cdot M is CP

#smul

Let RR be a commutative semiring, and let AA and BB be finite types. If a linear map between matrix spaces M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) is completely positive, then for any scalar xRx \in R such that 0x0 \le x, the scaled map xMx \cdot M is also completely positive.

theorem

The zero matrix map is completely positive

#zero

Let RR be a semiring and A,BA, B be types representing the indices of square matrices. The zero linear map 0:MatA(R)MatB(R)0: \text{Mat}_A(R) \to \text{Mat}_B(R) is completely positive.

theorem

A sum of completely positive maps is completely positive

#finset_sum

Let RR be a commutative semiring and A,BA, B be finite types. Let ι\iota be a finite index set. If {Mi}iι\{M_i\}_{i \in \iota} is a collection of linear matrix maps Mi:MatA(R)MatB(R)M_i: \text{Mat}_A(R) \to \text{Mat}_B(R) such that each MiM_i is completely positive, then their sum iιMi\sum_{i \in \iota} M_i is also a completely positive map.

theorem

The map MMkCM \mapsto M \otimes_k C is completely positive if CC is positive semidefinite

#kron_kronecker_const

Let RR be a ring. Let CMatd(R)C \in \text{Mat}_d(R) be a positive semidefinite matrix. The matrix map MMkCM \mapsto M \otimes_k C, which maps a square matrix MMatA(R)M \in \text{Mat}_A(R) to the Kronecker product MkCMatA×d(R)M \otimes_k C \in \text{Mat}_{A \times d}(R), is a completely positive map.

theorem

The Choi matrix of a Kraus map Kk()Kk\sum K_k (\cdot) K_k^* equals vKkvKk\sum |v_{K_k}\rangle \langle v_{K_k}|

#choi_of_kraus

Let k\mathbb{k} be a field with a star operation (typically C\mathbb{C}) and let K:κMatB×A(k)K: \kappa \to \text{Mat}_{B \times A}(\mathbb{k}) be a family of Kraus operators. Let Φ\Phi be the linear matrix map defined by Φ(X)=kκKkXKk\Phi(X) = \sum_{k \in \kappa} K_k X K_k^*. Then the Choi matrix J(Φ)\mathcal{J}(\Phi) of this map is given by the sum of outer products: J(Φ)=kκvkvk\mathcal{J}(\Phi) = \sum_{k \in \kappa} |v_k\rangle \langle v_k| where each vkv_k is a vector indexed by B×AB \times A such that (vk)(j,i)=(Kk)j,i(v_k)_{(j, i)} = (K_k)_{j, i}, and vk\langle v_k| denotes the conjugate transpose of vkv_k (represented by the `star` operation on its entries).

definition

Matrix conjugation map xyxyHx \mapsto y x y^H

#conj

Given a matrix yy in MatB×A(R)\text{Mat}_{B \times A}(R), the function `MatrixMap.conj` returns the linear map from MatA(R)\text{Mat}_A(R) to MatB(R)\text{Mat}_B(R) defined by xyxyHx \mapsto y x y^H, where yHy^H denotes the conjugate transpose of yy.

theorem

The conjugation map XMXMX \mapsto M X M^\dagger is positive

#conj_isPositive

Let MM be a matrix in MatrixB×A(k)\text{Matrix}_{B \times A}(\mathbb{k}). Let ΦM\Phi_M be the matrix map defined by the conjugation ΦM(X)=MXM\Phi_M(X) = M X M^\dagger. Then the map ΦM\Phi_M is positive.

theorem

The sum of positive matrix maps is positive

#IsPositive_sum

Let ι\iota be a finite index set and let {fi}iι\{f_i\}_{i \in \iota} be a family of linear matrix maps fi:MatA(C)MatB(C)f_i: \text{Mat}_A(\mathbb{C}) \to \text{Mat}_B(\mathbb{C}). If each map fif_i is positive, then their sum iιfi\sum_{i \in \iota} f_i is also a positive matrix map.

theorem

The Kraus representation Φ(X)=kKkXKk\Phi(X) = \sum_k K_k X K_k^\dagger is a positive map

#of_kraus_isPositive

Let K={Kk}kκK = \{K_k\}_{k \in \kappa} be a family of matrices in MatB×A(C)\text{Mat}_{B \times A}(\mathbb{C}) indexed by a finite set κ\kappa. Let Φ\Phi be the matrix map defined by Φ(X)=kκKkXKk\Phi(X) = \sum_{k \in \kappa} K_k X K_k^\dagger. Then the map Φ\Phi is positive.

theorem

conj(M)kmconj(N)=conj(MkN)\text{conj}(M) \otimes_{km} \text{conj}(N) = \text{conj}(M \otimes_k N)

#conj_kron

Let k\mathbf{k} be a field. For any matrices MMatB×A(k)M \in \text{Mat}_{B \times A}(\mathbf{k}) and NMatD×C(k)N \in \text{Mat}_{D \times C}(\mathbf{k}), the Kronecker product of their associated congruence maps is equal to the congruence map associated with their Kronecker product. That is, conj(M)kmconj(N)=conj(MkN),\text{conj}(M) \otimes_{km} \text{conj}(N) = \text{conj}(M \otimes_k N), where conj(M)\text{conj}(M) denotes the matrix map XMXMX \mapsto MXM^\dagger, km\otimes_{km} denotes the Kronecker product of matrix maps, and k\otimes_k denotes the Kronecker product of matrices.

theorem

The congruence map of the identity matrix is the identity map id\text{id}

#congruence_one_eq_id

Let IAI_A be the identity matrix in the space of A×AA \times A complex matrices MatA(C)\text{Mat}_A(\mathbb{C}). The congruence matrix map associated with IAI_A, defined by the transformation XIAXIAX \mapsto I_A X I_A^\dagger, is equal to the identity matrix map id\text{id} on MatA(C)\text{Mat}_A(\mathbb{C}).

theorem

Congruence maps XMXMX \mapsto MXM^\dagger are completely positive

#congruence_CP

Let k\mathbf{k} be a field (typically R\mathbb{R} or C\mathbb{C}). For any finite types AA and BB, and any matrix MMatB×A(k)M \in \text{Mat}_{B \times A}(\mathbf{k}), the matrix map defined by the congruence transformation XMXMX \mapsto MXM^\dagger (denoted as `conj M`) is completely positive.

theorem

The sum of completely positive matrix maps is completely positive

#IsCompletelyPositive_sum

Let AA and BB be finite types and let MatA(C)CMatB(C)\text{Mat}_A(\mathbb{C}) \to_{\mathbb{C}} \text{Mat}_B(\mathbb{C}) denote the space of linear maps between square matrices over the complex numbers. For any finite collection of such matrix maps {fi}iι\{f_i\}_{i \in \iota}, if each individual map fif_i is completely positive, then their sum iιfi\sum_{i \in \iota} f_i is also completely positive.

theorem

The Kraus-form map Φ(X)=KkXKk\Phi(X) = \sum K_k X K_k^\dagger equals the sum of congruence maps conj(Kk)\sum \text{conj}(K_k)

#of_kraus_eq_sum_conj

Let k\mathbf{k} be a field and K:κMatB×A(k)K: \kappa \to \text{Mat}_{B \times A}(\mathbf{k}) be a family of matrices indexed by a finite set κ\kappa. The matrix map Φ\Phi defined by the Kraus representation Φ(X)=kκKkXKk\Phi(X) = \sum_{k \in \kappa} K_k X K_k^\dagger is equal to the sum of the congruence maps XKkXKkX \mapsto K_k X K_k^\dagger for each kκk \in \kappa.

theorem

Matrix maps in Kraus form XKkXKkX \mapsto \sum K_k X K_k^* are completely positive

#of_kraus_CP

Let k\mathbf{k} be a field and A,BA, B be finite types. For any family of matrices {Kk}kκ\{K_k\}_{k \in \kappa} where KkMatB×A(k)K_k \in \text{Mat}_{B \times A}(\mathbf{k}), the linear matrix map Φ:MatA(k)MatB(k)\Phi: \text{Mat}_A(\mathbf{k}) \to \text{Mat}_B(\mathbf{k}) defined in Kraus form by Φ(X)=kκKkXKk\Phi(X) = \sum_{k \in \kappa} K_k X K_k^* is completely positive.

theorem

A PSD matrix CC is the Choi matrix of some Kraus-form map Φ(X)=KkXKk\Phi(X) = \sum K_k X K_k^*

#exists_kraus_of_choi_PSD

Let k\mathbf{k} be a field with a star operation, and let AA and BB be finite index sets. For any matrix CMat(B×A)×(B×A)(k)C \in \text{Mat}_{(B \times A) \times (B \times A)}(\mathbf{k}), if CC is positive semidefinite (C0C \succeq 0), then there exists a family of matrices {Kk}kB×A\{K_k\}_{k \in B \times A} with KkMatB×A(k)K_k \in \text{Mat}_{B \times A}(\mathbf{k}) such that CC is the Choi matrix of the linear map Φ(X)=kKkXKk\Phi(X) = \sum_k K_k X K_k^*.

theorem

J(M)=(Mid)(Ei,jEi,j)\mathcal{J}(M) = (M \otimes \text{id})(\sum E_{i,j} \otimes E_{i,j})

#choi_matrix_eq_map_proj

Let RR be a semiring and M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between square matrix spaces. The Choi matrix J(M)\mathcal{J}(M) of the map MM is equal to the result of applying the extended map MkmidAM \otimes_{km} \text{id}_A to the matrix ΩΩ|\Omega\rangle\langle\Omega|, where ΩΩ|\Omega\rangle\langle\Omega| is the matrix in MatA×A(R)\text{Mat}_{A \times A}(R) defined by the outer product of the vector ieiei\sum_i e_i \otimes e_i with its adjoint. Specifically, J(M)=(MkmidA)(i,jEi,jEi,j)\mathcal{J}(M) = (M \otimes_{km} \text{id}_A) \left( \sum_{i, j} E_{i, j} \otimes E_{i, j} \right) where idA\text{id}_A is the identity map on MatA(R)\text{Mat}_A(R), km\otimes_{km} denotes the Kronecker product of matrix maps, and the input matrix has entries δi1,i2δj1,j2\delta_{i_1, i_2} \overline{\delta_{j_1, j_2}} at index ((i1,j1),(i2,j2))((i_1, j_1), (i_2, j_2)).

theorem

MM is completely positive     J(M)\iff \mathcal{J}(M) is positive semidefinite

#choi_PSD_iff_CP_map

Let RR be a semiring and A,BA, B be finite types. For a linear map between square matrix spaces M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_{B}(R), MM is completely positive if and only if its Choi matrix J(M)\mathcal{J}(M) is positive semidefinite.

theorem

conj y=LyRy\text{conj } y = L_y \circ R_{y^\dagger}

#conj_eq_mulRightLinearMap_comp_mulRightLinearMap

Let RR be a star-semiring and yy be a B×AB \times A matrix over RR. Let conj y\text{conj } y denote the matrix map defined by XyXyX \mapsto y X y^\dagger, where yy^\dagger is the conjugate transpose of yy. This map is equal to the composition of the left-multiplication map by yy and the right-multiplication map by yy^\dagger. That is, conj y=LyRy \text{conj } y = L_y \circ R_{y^\dagger} where Ly(X)=yXL_y(X) = yX and Ry(X)=XyR_{y^\dagger}(X) = Xy^\dagger.

theorem

Matrix conjugation XMXMX \mapsto M X M^* is completely positive

#conj_isCompletelyPositive

Let RR be a star-ordered ring (such as C\mathbb{C}) and let B,AB, A be finite types. For any matrix MMatB×A(R)M \in \text{Mat}_{B \times A}(R), the conjugation map defined by XMXMX \mapsto M X M^* (denoted as `conj M`) is a completely positive map from MatA(R)\text{Mat}_A(R) to MatB(R)\text{Mat}_B(R).

theorem

The matrix submatrix map MMf,fM \mapsto M_{f,f} is completely positive

#submatrix

Let RR be a semiring and f:BAf: B \to A be a function between finite index sets. The linear map MatA(R)MatB(R)\text{Mat}_A(R) \to \text{Mat}_B(R) defined by taking the submatrix MMf,fM \mapsto M_{f,f}, where (Mf,f)i,j=Mf(i),f(j)(M_{f,f})_{i,j} = M_{f(i), f(j)} for all i,jBi, j \in B, is completely positive.

theorem

A linear map defined by Kraus operators MkM_k is completely positive

#of_kraus_isCompletelyPositive

Let RR be a star-semiring and let AA and BB be finite index sets. Given a family of matrices MkMatB×A(R)M_k \in \text{Mat}_{B \times A}(R) indexed by kκk \in \kappa, the linear map Φ:MatA×A(R)MatB×B(R)\Phi: \text{Mat}_{A \times A}(R) \to \text{Mat}_{B \times B}(R) defined by Φ(X)=kκMkXMk\Phi(X) = \sum_{k \in \kappa} M_k X M_k^* is completely positive, where MkM_k^* denotes the conjugate transpose of MkM_k.

theorem

The Choi matrix of a map in symmetric Kraus form Φ(X)=KkXKk\Phi(X) = \sum K_k X K_k^* is vkvk\sum |v_k\rangle \langle v_k|

#choi_of_kraus_R

Let k\mathbf{k} be a star-ring and let A,BA, B be finite index sets. Given a family of matrices KkMatB×A(k)K_k \in \text{Mat}_{B \times A}(\mathbf{k}) indexed by kκk \in \kappa, let Φ\Phi be the linear map defined in symmetric Kraus form by Φ(X)=kκKkXKk\Phi(X) = \sum_{k \in \kappa} K_k X K_k^*. Then the Choi matrix J(Φ)\mathcal{J}(\Phi) is given by the sum of outer products: J(Φ)=kκvkvk\mathcal{J}(\Phi) = \sum_{k \in \kappa} |v_k\rangle \langle v_k| where vkv_k is the vectorization of the matrix KkK_k, such that for (j,i)B×A(j, i) \in B \times A, the component is (vk)(j,i)=(Kk)j,i(v_k)_{(j, i)} = (K_k)_{j, i}, and (vkvk)(j1,i1),(j2,i2)=(Kk)j1,i1(Kk)j2,i2(|v_k\rangle \langle v_k|)_{(j_1, i_1), (j_2, i_2)} = (K_k)_{j_1, i_1} \cdot \overline{(K_k)_{j_2, i_2}}.

theorem

Choi matrix J(M)\mathcal{J}(M) equals (Mkmid)(J(id))(M \otimes_{km} \text{id})(\mathcal{J}(\text{id}))

#choi_eq_kron_id_apply_choi_id

Let RR be a commutative semiring and M:MatA(R)MatB(R)M: \text{Mat}_{A}(R) \to \text{Mat}_{B}(R) be a linear map between square matrix spaces. The Choi matrix of MM, denoted J(M)\mathcal{J}(M), is equal to the result of applying the Kronecker product of MM and the identity map idA\text{id}_A to the Choi matrix of the identity map on MatA(R)\text{Mat}_A(R). That is, J(M)=(MkmidA)(J(idA))\mathcal{J}(M) = (M \otimes_{km} \text{id}_A)(\mathcal{J}(\text{id}_A)) where km\otimes_{km} denotes the Kronecker product of matrix maps.

theorem

The Choi matrix of the identity map is positive semidefinite.

#choi_id_is_PSD

Let RR be a field of either real or complex numbers (R\mathbb{R} or C\mathbb{C}), and let AA be a finite index set. Let id:MatA(R)MatA(R)\text{id}: \text{Mat}_A(R) \to \text{Mat}_A(R) be the identity matrix map. Then the Choi matrix of the identity map, J(id)\mathcal{J}(\text{id}), is a positive semidefinite matrix.

theorem

If MM is completely positive, then its Choi matrix is positive semidefinite

#is_CP_implies_choi_PSD

Let RR be a real or complex field (an `RCLike` ring), and let AA and BB be finite types. Let M:MatA(R)MatB(R)M: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear map between square matrix spaces. If MM is completely positive, then its Choi matrix J(M)MatB×A(R)\mathcal{J}(M) \in \text{Mat}_{B \times A}(R) is positive semidefinite.

theorem

Completely Positive Maps Admit a Kraus Representation

#exists_kraus

Let RR be a conjugate-transposable ring (typically C\mathbb{C} or R\mathbb{R}) and A,BA, B be finite types indexing square matrices. Let Φ:MatA(R)MatB(R)\Phi: \text{Mat}_A(R) \to \text{Mat}_B(R) be a linear matrix map. If Φ\Phi is completely positive, then there exists a family of matrices MkMatB×A(R)M_k \in \text{Mat}_{B \times A}(R) indexed by kB×Ak \in B \times A such that Φ\Phi can be represented in Kraus form: Φ(X)=kMkXMk\Phi(X) = \sum_{k} M_k X M_k^* where MkM_k^* denotes the conjugate transpose of MkM_k.

theorem

ΦMkmΦN=ΦMN\Phi_M \otimes_{km} \Phi_N = \Phi_{M \otimes N}

#kron_of_kraus

Let RR be a commutative star-semiring. Given two families of Kraus operators M:κMatB×A(R)M: \kappa \to \text{Mat}_{B \times A}(R) and N:ιMatD×C(R)N: \iota \to \text{Mat}_{D \times C}(R), the Kronecker product of their associated Kraus maps is equal to the Kraus map generated by the Kronecker products of the individual operators. That is, ΦMkmΦN=ΦMN \Phi_M \otimes_{km} \Phi_N = \Phi_{M \otimes N} where the resulting Kraus map ΦMN\Phi_{M \otimes N} is defined by the family of operators (MkkNi)(M_k \otimes_k N_i) indexed by (k,i)κ×ι(k, i) \in \kappa \times \iota.

theorem

M1M_1 is CP and M2M_2 is CP     M1kmM2\implies M_1 \otimes_{km} M_2 is CP

#kron

Let RR be a commutative semiring and A,B,C,DA, B, C, D be finite types indexing square matrices. Let M1:MatA(R)MatB(R)M_1: \text{Mat}_A(R) \to \text{Mat}_B(R) and M2:MatC(R)MatD(R)M_2: \text{Mat}_C(R) \to \text{Mat}_D(R) be linear matrix maps. If M1M_1 and M2M_2 are both completely positive, then their Kronecker product M1kmM2:MatA×C(R)MatB×D(R)M_1 \otimes_{km} M_2: \text{Mat}_{A \times C}(R) \to \text{Mat}_{B \times D}(R) is also completely positive.

theorem

The tensor product iιΛi\bigotimes_{i \in \iota} \Lambda_i is completely positive if each Λi\Lambda_i is completely positive

#piProd

Let RR be a *-semiring. For a given family of matrix maps Λi:MatdIi(R)MatdOi(R)\Lambda_i: \text{Mat}_{dI_i}(R) \to \text{Mat}_{dO_i}(R) indexed by iιi \in \iota, if every map Λi\Lambda_i is completely positive, then their tensor product iιΛi\bigotimes_{i \in \iota} \Lambda_i is also a completely positive map from MatdIi(R)\text{Mat}_{\prod dI_i}(R) to MatdOi(R)\text{Mat}_{\prod dO_i}(R).