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QuantumInfo.Finite.Entropy.DPI

DPI (Data Processing Inequality)

The Data Processing Inequality (DPI) for the sandwiched Rényi relative entropy, and as a consequence, the quantum relative entropy.

35 declarations

definition

The Weighted Norm Xp,σ\|X\|_{p, \sigma}

Given a set of d×dd \times d complex matrices, for pRp \in \mathbb{R} and a quantum state σ\sigma (represented as a density matrix), the weighted norm p,σ\|\cdot\|_{p, \sigma} is defined for a matrix XX as Xp,σ=σ1/(2p)Xσ1/(2p)p \|X\|_{p, \sigma} = \left\| \sigma^{1/(2p)} X \sigma^{1/(2p)} \right\|_p where p\|\cdot\|_p denotes the Schatten pp-norm and σ1/(2p)\sigma^{1/(2p)} is defined via the continuous functional calculus for Hermitian matrices.

definition

Spectral norm of a matrix AA (as maximum eigenvalue of AA\sqrt{A^\dagger A})

For a matrix AMatd×d(C)A \in \text{Mat}_{d \times d}(\mathbb{C}), the spectral norm (or operator norm) A\|A\|_\infty is defined as the square root of the maximum eigenvalue of the matrix AAA^\dagger A, where AA^\dagger denotes the conjugate transpose of AA. If the index set of the matrix is empty, the norm is defined to be 00.

definition

Weighted \infty-norm of a matrix equals its spectral norm

For a quantum state σ\sigma represented as a density matrix in MState d\text{MState } d and a complex matrix XMatd×d(C)X \in \text{Mat}_{d \times d}(\mathbb{C}), the weighted norm for p=p = \infty is defined as the spectral norm X\|X\|_\infty of the matrix XX. In this case, the norm is independent of the state σ\sigma.

definition

The Map Γσ(X)=σ1/2Xσ1/2\Gamma_\sigma(X) = \sigma^{1/2} X \sigma^{1/2}

Given a quantum state σ\sigma represented as a density matrix in Matd(C)\text{Mat}_d(\mathbb{C}), the map Γσ\Gamma_\sigma is defined for any matrix XMatd(C)X \in \text{Mat}_d(\mathbb{C}) as Γσ(X)=σ1/2Xσ1/2\Gamma_\sigma(X) = \sigma^{1/2} X \sigma^{1/2} where σ1/2\sigma^{1/2} is the unique positive semidefinite square root of σ\sigma obtained via the continuous functional calculus.

definition

The inverse map Γσ1(X)=σ1/2Xσ1/2\Gamma_\sigma^{-1}(X) = \sigma^{-1/2} X \sigma^{-1/2}

Given a quantum state σ\sigma (represented as a density matrix in Cd×d\mathbb{C}^{d \times d}) and a complex matrix XCd×dX \in \mathbb{C}^{d \times d}, the map Γσ1\Gamma_\sigma^{-1} is defined as Γσ1(X)=σ1/2Xσ1/2\Gamma_\sigma^{-1}(X) = \sigma^{-1/2} X \sigma^{-1/2} where σ1/2\sigma^{-1/2} is computed via the continuous functional calculus for Hermitian matrices using the power function xx1/2x \mapsto x^{-1/2}.

definition

The operator T=ΓΦ(σ)1ΦΓσT = \Gamma_{\Phi(\sigma)}^{-1} \circ \Phi \circ \Gamma_\sigma associated with a CPTP map Φ\Phi and state σ\sigma

Given a completely positive trace-preserving (CPTP) map Φ:Matd(C)Matd2(C)\Phi: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_{d_2}(\mathbb{C}) and a density matrix (state) σMState(d)\sigma \in \text{MState}(d), the operator TΦ,σ:Matd(C)Matd2(C)T_{\Phi, \sigma}: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_{d_2}(\mathbb{C}) is defined by the composition TΦ,σ=ΓΦ(σ)1ΦΓσT_{\Phi, \sigma} = \Gamma_{\Phi(\sigma)}^{-1} \circ \Phi \circ \Gamma_\sigma Specifically, for an input matrix XMatd(C)X \in \text{Mat}_d(\mathbb{C}), the value is TΦ,σ(X)=ΓΦ(σ)1(Φ(Γσ(X)))T_{\Phi, \sigma}(X) = \Gamma_{\Phi(\sigma)}^{-1}(\Phi(\Gamma_\sigma(X))), where Γσ\Gamma_\sigma is a map associated with the state σ\sigma and ΓΦ(σ)1\Gamma_{\Phi(\sigma)}^{-1} is the inverse map associated with the evolved state Φ(σ)\Phi(\sigma).

definition

Induced norm of a map Ψ\Psi with respect to weighted pp-norms p,σ\|\cdot\|_{p, \sigma} and p,Φ(σ)\|\cdot\|_{p, \Phi(\sigma)}

Let Md\mathcal{M}_d denote the space of d×dd \times d complex matrices and MState(d)\text{MState}(d) denote the space of density matrices (quantum states). Given a real number pp, a reference state σMState(d)\sigma \in \text{MState}(d), a CPTP (completely positive trace-preserving) map Φ:MdMd2\Phi: \mathcal{M}_d \to \mathcal{M}_{d_2}, and a linear map Ψ:MdMd2\Psi: \mathcal{M}_d \to \mathcal{M}_{d_2}, the induced norm (with respect to weighted pp-norms) is defined as the supremum: Ψp,σΦ(σ)=supXMd,Xp,σ0Ψ(X)p,Φ(σ)Xp,σ\|\Psi\|_{p, \sigma \to \Phi(\sigma)} = \sup_{X \in \mathcal{M}_d, \|X\|_{p, \sigma} \neq 0} \frac{\|\Psi(X)\|_{p, \Phi(\sigma)}}{\|X\|_{p, \sigma}} where p,σ\| \cdot \|_{p, \sigma} denotes the weighted pp-norm relative to the state σ\sigma.

definition

Induced infinity-norm of a map Ψ\Psi relative to states σ\sigma and Φ(σ)\Phi(\sigma)

Given a mixed state σMState(d)\sigma \in \text{MState}(d), a completely positive trace-preserving (CPTP) map Φ:MState(d)MState(d2)\Phi: \text{MState}(d) \to \text{MState}(d_2), and a linear map between matrices Ψ:Matd(C)Matd2(C)\Psi: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_{d_2}(\mathbb{C}), the induced infinity-norm ΨσΦ(σ),\left\| \Psi \right\|_{\sigma \to \Phi(\sigma), \infty} is defined as the supremum of the ratio of weighted infinity-norms: supX0Ψ(X),Φ(σ)X,σ\sup_{X \neq 0} \frac{\| \Psi(X) \|_{\infty, \Phi(\sigma)}}{\| X \|_{\infty, \sigma}} where ,ρ\| \cdot \|_{\infty, \rho} denotes the weighted infinity-norm relative to a state ρ\rho.

definition

Linear map TT associated with state σ\sigma and CPTP map Φ\Phi

Given a quantum state σ\sigma (represented as a density matrix in Matd(C)\text{Mat}_d(\mathbb{C})) and a completely positive trace-preserving (CPTP) map Φ:Matd(C)Matd2(C)\Phi: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_{d_2}(\mathbb{C}), the map Tσ,ΦT_{\sigma, \Phi} is the linear map from Matd(C)\text{Mat}_d(\mathbb{C}) to Matd2(C)\text{Mat}_{d_2}(\mathbb{C}) defined by the operation TopT_{\text{op}}. Specifically, it is the linear map version of the operator XΦ(Γσ(X))X \mapsto \Phi(\Gamma_\sigma(X)) or its related variants used in the study of Rényi relative entropy and the Data Processing Inequality.

definition

The matrix conjugation map Xσ1/2Xσ1/2X \mapsto \sigma^{1/2} X \sigma^{1/2}

Let dd be a finite-dimensional Hilbert space index and σ\sigma be a quantum state (density matrix) in MStated\text{MState}_d. Let σ1/2\sigma^{1/2} denote the square root of the matrix σ\sigma, calculated via the continuous functional calculus. The linear map Γσ\Gamma_\sigma is defined as the matrix conjugation map from Matd(C)\text{Mat}_d(\mathbb{C}) to Matd(C)\text{Mat}_d(\mathbb{C}) given by Xσ1/2Xσ1/2X \mapsto \sigma^{1/2} X \sigma^{1/2}.

theorem

The linear map Γmap(σ)\Gamma_{\text{map}}(\sigma) evaluated at XX equals Γ(σ,X)\Gamma(\sigma, X)

For a quantum mixed state σ\sigma of dimension dd and any d×dd \times d complex matrix XX, the evaluation of the linear map Γmap(σ)\Gamma_{\text{map}}(\sigma) at XX is equal to the matrix Γ(σ,X)\Gamma(\sigma, X).

theorem

The map Γσ\Gamma_{\sigma} is completely positive

Let σ\sigma be a quantum state (represented as an element of `MState d`). The associated linear map Γσ\Gamma_{\sigma}, defined by Γσ(X)=σ1/2Xσ1/2\Gamma_{\sigma}(X) = \sigma^{1/2} X \sigma^{1/2}, is a completely positive matrix map.

definition

Inverse Gamma Map Γσ1\Gamma_{\sigma}^{-1} as a conjugation by σ1/2\sigma^{-1/2}

Let σ\sigma be a quantum state represented by a matrix MσM_\sigma in the space of d×dd \times d complex matrices. The linear map Γσ1:Matd(C)Matd(C)\Gamma_{\sigma}^{-1}: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_d(\mathbb{C}) is defined as the matrix conjugation map XMσ1/2X(Mσ1/2)HX \mapsto M_\sigma^{-1/2} X (M_\sigma^{-1/2})^H. Here, Mσ1/2M_\sigma^{-1/2} is obtained by applying the continuous functional calculus to the matrix MσM_\sigma with the power function f(x)=x1/2f(x) = x^{-1/2}.

theorem

The matrix map Γσ1\Gamma_{\sigma}^{-1} equals the function Gamma_inv\text{Gamma\_inv} for any state σ\sigma and matrix XX

Let σ\sigma be a quantum mixed state of dimension dd and XX be a d×dd \times d complex matrix. The evaluation of the inverse Γ\Gamma-map of σ\sigma, denoted as Γσ1\Gamma_{\sigma}^{-1}, at the matrix XX is equal to the matrix-defined operation Gamma_inv(σ,X)\text{Gamma\_inv}(\sigma, X).

definition

Inverse square root of a density matrix σ1/2\sigma^{-1/2}

Given a mixed state σ\sigma (represented as a density matrix MM in the space of d×dd \times d complex matrices), σ1/2\sigma^{-1/2} is defined as the matrix obtained by applying the continuous functional calculus to MM with the function f(x)=x1/2f(x) = x^{-1/2}.

theorem

Γσ1\Gamma_\sigma^{-1} is the conjugation map Xσ1/2Xσ1/2X \mapsto \sigma^{-1/2} X \sigma^{-1/2}

Let σ\sigma be a quantum state (represented as a density matrix of type `MState d`). The linear map Γσ1\Gamma_\sigma^{-1} (denoted as `Gamma_inv_map σ`) is equal to the matrix conjugation map Xσ1/2X(σ1/2)HX \mapsto \sigma^{-1/2} X (\sigma^{-1/2})^H, where σ1/2\sigma^{-1/2} is the inverse square root of the density matrix σ\sigma (denoted as `sigma_inv_sqrt σ`).

theorem

The map Γσ1\Gamma_{\sigma}^{-1} is completely positive

Let σ\sigma be a quantum state (density matrix) of dimension dd. The linear map Γσ1:Matd(C)Matd(C)\Gamma_{\sigma}^{-1}: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_d(\mathbb{C}), defined as the inverse of the map associated with σ\sigma, is completely positive.

theorem

Representation of Tσ,ΦT_{\sigma, \Phi} as the composite ΓΦ(σ)1ΦΓσ\Gamma_{\Phi(\sigma)}^{-1} \circ \Phi \circ \Gamma_{\sigma}

Let σ\sigma be a quantum state (density matrix) in the state space MState(d)\text{MState}(d) and Φ:MState(d)MState(d2)\Phi: \text{MState}(d) \to \text{MState}(d_2) be a completely positive trace-preserving (CPTP) map with underlying linear map Φmap\Phi_{\text{map}}. Let Γσ\Gamma_{\sigma} denote the map Xσ1/2Xσ1/2X \mapsto \sigma^{1/2} X \sigma^{1/2} and Γσ1\Gamma_{\sigma}^{-1} denote the map Xσ1/2Xσ1/2X \mapsto \sigma^{-1/2} X \sigma^{-1/2}. The map Tσ,ΦT_{\sigma, \Phi} is equal to the composition of the linear maps ΓΦ(σ)1ΦmapΓσ\Gamma_{\Phi(\sigma)}^{-1} \circ \Phi_{\text{map}} \circ \Gamma_{\sigma}.

theorem

The map Tσ,ΦT_{\sigma, \Phi} is completely positive

Let σMState(d)\sigma \in \text{MState}(d) be a matrix state and Φ:MState(d)MState(d2)\Phi: \text{MState}(d) \to \text{MState}(d_2) be a completely positive trace-preserving (CPTP) map. Let Tσ,ΦT_{\sigma, \Phi} be the linear matrix map defined by Tσ,Φ=ΓΦ(σ)1ΦΓσT_{\sigma, \Phi} = \Gamma_{\Phi(\sigma)}^{-1} \circ \Phi \circ \Gamma_{\sigma}, where Γ\Gamma and Γ1\Gamma^{-1} are specific maps associated with the states. Then the map Tσ,ΦT_{\sigma, \Phi} is completely positive.

theorem

Positivity of the Tσ,ΦT_{\sigma, \Phi} map

Let σ\sigma be a quantum state (represented as an element of `MState d`) and Φ\Phi be a Completely Positive Trace Preserving (CPTP) map from dimension dd to d2d_2. Then the associated matrix map Tσ,ΦT_{\sigma, \Phi} (defined as `T_map σ Φ`) is a positive map. Specifically, for any positive semi-definite matrix xx, Tσ,Φ(x)T_{\sigma, \Phi}(x) is also positive semi-definite.

theorem

The Weighted 11-Norm of XX with respect to σ\sigma equals the Trace Norm of Γσ(X)\Gamma_\sigma(X)

Let σ\sigma be a mixed quantum state of dimension dd and XX be a d×dd \times d complex matrix. The weighted norm of XX with respect to σ\sigma for p=1p = 1, denoted as weighted_norm(1,σ,X)\text{weighted\_norm}(1, \sigma, X), is equal to the trace norm (Schatten 11-norm) of the matrix Γσ(X)\Gamma_\sigma(X), where Γσ(X)\Gamma_\sigma(X) is the operator defined by the action of σ\sigma on XX. Mathematically, this is expressed as: weighted_norm(1,σ,X)=Γσ(X)1\text{weighted\_norm}(1, \sigma, X) = \|\Gamma_\sigma(X)\|_1

definition

(p,q)(p, q)-induced norm of a linear map Ψ\Psi relative to states σ,σ\sigma, \sigma'

Let dd and d2d_2 be finite-dimensional Hilbert space dimensions. Given two quantum states σMState(d)\sigma \in \text{MState}(d) and σMState(d2)\sigma' \in \text{MState}(d_2), and a linear map between matrix spaces Ψ:Matd(C)Matd2(C)\Psi: \text{Mat}_d(\mathbb{C}) \to \text{Mat}_{d_2}(\mathbb{C}), the (p,q)(p, q)-induced norm of Ψ\Psi relative to σ\sigma and σ\sigma' is defined as: Ψpq,σσ=supXMatd(C),Xp,σ0Ψ(X)q,σXp,σ\|\Psi\|_{p \to q, \sigma \to \sigma'} = \sup_{X \in \text{Mat}_d(\mathbb{C}), \|X\|_{p, \sigma} \neq 0} \frac{\|\Psi(X)\|_{q, \sigma'}}{\|X\|_{p, \sigma}} where p,σ\| \cdot \|_{p, \sigma} denotes the weighted norm with respect to the state σ\sigma.

theorem

The Continuous Functional Calculus for Hermitian Matrices is Multiplicative

Let dd be a finite type with decidable equality. For any Hermitian matrix ACd×dA \in \mathbb{C}^{d \times d} and any functions f,g:RRf, g: \mathbb{R} \to \mathbb{R}, the product of the matrices obtained by applying the continuous functional calculus (CFC) to AA with functions ff and gg is equal to the matrix obtained by applying the continuous functional calculus to AA with the pointwise product function fgf \cdot g. That is, f(A)g(A)=(fg)(A) f(A) \cdot g(A) = (f \cdot g)(A) where f(A)f(A) denotes the result of the functional calculus applied to AA.

theorem

Γσ(I)=σ.M.mat\Gamma_\sigma(I) = \sigma.M.\text{mat}

For any quantum mixed state σ\sigma of finite dimension dd, the operator Γσ\Gamma_\sigma applied to the identity matrix II is equal to the underlying matrix representation of the state σ\sigma, denoted as σ.M.mat\sigma.M.\text{mat}.

theorem

Γσ1(Mσ)=1\Gamma_\sigma^{-1}(M_\sigma) = \mathbb{1} for positive definite σ\sigma

Let σ\sigma be a quantum mixed state of dimension dd with a positive definite density matrix m(σ)m(\sigma). Let MσM_\sigma be the Hermitian operator associated with σ\sigma, and let Γσ1\Gamma_\sigma^{-1} be the inverse of the map Γσ\Gamma_\sigma. Then applying Γσ1\Gamma_\sigma^{-1} to the matrix representation of MσM_\sigma yields the identity matrix, i.e., Γσ1(Mσ)=1\Gamma_\sigma^{-1}(M_\sigma) = \mathbb{1}.

theorem

Matrix Representation of CPTP Map Φ\Phi acting on State σ\sigma

Let Φ\Phi be a completely positive trace-preserving (CPTP) map from the space of density matrices Md\mathcal{M}_d to Md2\mathcal{M}_{d_2}. For any matrix state σ\sigma of dimension dd, the underlying matrix of the image Φ(σ)\Phi(\sigma) is equal to the underlying linear map of Φ\Phi applied to the matrix σ.M.mat\sigma.M.mat. That is, (Φσ).M.mat=Φ.map(σ.M.mat)(\Phi \sigma).M.mat = \Phi.map(\sigma.M.mat).

theorem

The map Tσ,ΦT_{\sigma, \Phi} is unital if Φ(σ)>0\Phi(\sigma) > 0.

Let σ\sigma be a quantum state (matrix state) of dimension dd, and let Φ\Phi be a completely positive trace-preserving (CPTP) map from d×dd \times d matrices to d2×d2d_2 \times d_2 matrices. Suppose that the image of the state under the map, Φ(σ)\Phi(\sigma), is positive definite. Then the associated linear map Tσ,ΦT_{\sigma, \Phi} is unital, i.e., Tσ,Φ(I)=IT_{\sigma, \Phi}(\mathbb{I}) = \mathbb{I}, where I\mathbb{I} denotes the identity matrix.

theorem

The map Tσ,ΦT_{\sigma, \Phi} is completely positive

Let σ\sigma be a mixed state (density matrix) in a dd-dimensional Hilbert space, and let Φ\Phi be a Completely Positive Trace-Preserving (CPTP) map from dd-dimensional matrices to d2d_2-dimensional matrices. Then the associated map Tσ,ΦT_{\sigma, \Phi} (often referred to as the Petz recovery map or a related transition map in the context of the Data Processing Inequality) is a completely positive matrix map.

theorem

ΓσΓσ1\Gamma_{\sigma} \circ \Gamma_{\sigma}^{-1} is the Identity on Matrices for Positive Definite σ\sigma

Let σ\sigma be a quantum mixed state of dimension dd such that its underlying matrix σ.m\sigma.m is positive definite. For any d×dd \times d complex matrix XX, the composition of the map Γσ1\Gamma_{\sigma}^{-1} followed by Γσ\Gamma_{\sigma} is the identity, such that Γσ(Γσ1(X))=X\Gamma_{\sigma}(\Gamma_{\sigma}^{-1}(X)) = X.

theorem

AMI    λi(A)MA \le M I \implies \lambda_i(A) \le M

Let AA be a Hermitian matrix of dimension dd and MM be a real number. If AMIA \le M I, where II is the identity matrix and \le denotes the Loewner order (the positive semi-definite order), then every eigenvalue λi\lambda_i of AA satisfies λiM\lambda_i \le M.

theorem

If the eigenvalues of AA are bounded by MM, then AMIA \leq M \cdot I

Let AA be a Hermitian matrix of dimension dd over the complex numbers C\mathbb{C}. Given a real number MM, if all eigenvalues λi\lambda_i of AA satisfy λiM\lambda_i \leq M for all indices ii, then AA is less than or equal to MM times the identity matrix II in the Loewner order, i.e., AMIA \leq M \cdot I.

theorem

The Block Matrix of Gramian-like Products (YY,YX,XY,XX)(Y^\dagger Y, Y^\dagger X, X^\dagger Y, X^\dagger X) is Positive Semidefinite

Let m,n,km, n, k be finite types. For any complex matrices XCk×nX \in \mathbb{C}^{k \times n} and YCk×mY \in \mathbb{C}^{k \times m}, the block matrix (YYYXXYXX) \begin{pmatrix} Y^\dagger Y & Y^\dagger X \\ X^\dagger Y & X^\dagger X \end{pmatrix} is positive semidefinite, where MM^\dagger denotes the conjugate transpose of a matrix MM.

theorem

The Block Matrix with Identity and XXX^\dagger X is Positive Semidefinitet

Let mm and nn be finite types. For any m×nm \times n complex matrix XX, the block matrix (IXXXX) \begin{pmatrix} I & X \\ X^\dagger & X^\dagger X \end{pmatrix} is positive semidefinite, where II is the m×mm \times m identity matrix and XX^\dagger denotes the conjugate transpose of XX.

theorem

Data Processing Inequality for Sandwiched Rényi Relative Entropy DαD_{\alpha}

Let ρ\rho and σ\sigma be two quantum states (density matrices) in a finite-dimensional Hilbert space of dimension dd. For any parameter α1\alpha \ge 1 and any completely positive trace-preserving (CPTP) map Φ\Phi from states of dimension dd to states of dimension d2d_2, the sandwiched Rényi relative entropy DαD_{\alpha} satisfies the inequality: Dα(Φ(ρ)Φ(σ))Dα(ρσ)D_{\alpha}(\Phi(\rho) \| \Phi(\sigma)) \le D_{\alpha}(\rho \| \sigma) where Dα(ρσ)D_{\alpha}(\rho \| \sigma) denotes the sandwiched Rényi relative entropy between ρ\rho and σ\sigma.

axiom

Data Processing Inequality for Sandwiched Rényi Relative Entropy

For any real number α1\alpha \ge 1, any matrix states ρ\rho and σ\sigma in a Hilbert space of dimension dd, and any completely positive trace-preserving (CPTP) map Φ\Phi from dimension dd to dimension d2d_2, the sandwiched Rényi relative entropy satisfies the inequality: D~α(Φ(ρ)Φ(σ))D~α(ρσ) \tilde{D}_\alpha(\Phi(\rho) \| \Phi(\sigma)) \le \tilde{D}_\alpha(\rho \| \sigma) where D~α\tilde{D}_\alpha denotes the sandwiched Rényi relative entropy of order α\alpha.