Quantum Information and Channels: A formalization blueprint

17 Ambient Sources and Density Reduction

Local endpoint maps transport the Gaussian product source into ambient spaces while preserving its expectation. A separate contraction calculation bounds the physical density error when selected gates are approximated and rescaled. The input and output vectors retain their subnormalization. These calculations give the source and error estimates used in [ Ope26a , Theorem 5.2 ] .

17.1 Ambient endpoint coordinates

Let \(A,C,W,X,Y,Z\) be finite index sets. Choose arbitrary fixed endpoint maps \(E:\mathbb {C}^A\to \mathbb {C}^W\), \(F:\mathbb {C}^A\to \mathbb {C}^X\), \(\widetilde E:\mathbb {C}^C\to \mathbb {C}^Y\), and \(\widetilde F:\mathbb {C}^C\to \mathbb {C}^Z\). For nonnegative real weights \(\lambda ,\mu \), define

\begin{align} |\eta _{E,F}\rangle & =\sum _a\sqrt{\lambda _a}\, E|a\rangle \otimes F|a\rangle , \label{eq:reduction_ambient_vector} \\ X_{\mathrm{amb}} & =|\eta _{E,F}\rangle \langle \widetilde\eta _{\widetilde E,\widetilde F}|, \label{eq:reduction_ambient_target} \\ \mathcal T(M) & =(E\otimes F)M(\widetilde E\otimes \widetilde F)^\dagger . \label{eq:reduction_ambient_transport} \end{align}

The second ambient vector uses the weights \(\mu \). For the coordinate sampled factors of Definition 15.1.1, put \(U_j^{\mathrm{amb}}=EU_j\widetilde E^\dagger \) and \(V_j^{\mathrm{amb}}=FV_j\widetilde F^\dagger \). Then

\begin{align} \mathcal T(U\otimes V) & =(EU\widetilde E^\dagger )\otimes (FV\widetilde F^\dagger ), \label{eq:reduction_ambient_product} \\ \widehat X_{\mathrm{amb}} & =\frac1k\sum _{j=0}^{k-1}U_j^{\mathrm{amb}}\otimes V_j^{\mathrm{amb}} =\mathcal T(\widehat X), \label{eq:reduction_ambient_sample} \\ X_{\mathrm{amb}} & =\mathcal T(X), \label{eq:reduction_ambient_exact} \\ \Delta _{\mathrm{amb}} & =\widehat X_{\mathrm{amb}}-X_{\mathrm{amb}}=\mathcal T(\Delta ). \label{eq:reduction_ambient_correction} \end{align}

In particular, the represented ambient vector is the endpoint transport of its paired coordinate Schmidt vector. These are the ambient-coordinate forms of [ Ope26a , Equation (5.6) ] .

Theorem 17.1.2 Rank-one expansion of transported sources
✓

Let \(A,C\) be finite index sets and let \(W,X,Y,Z\) be arbitrary index sets. For endpoint matrices \(E,F,\widetilde E,\widetilde F\) of the indicated sizes, define the coordinate arrays \(u_{ab}(w,x)=E_{wa}F_{xb}\) and \(v_{cd}(y,z)=\widetilde E_{yc}\widetilde F_{zd}\). Every matrix \(M\) with rows indexed by \(A\times A\) and columns indexed by \(C\times C\) satisfies

\begin{align} \mathcal T(M) & =\sum _{a,b\in A}\sum _{c,d\in C} M_{(a,b),(c,d)}\, u_{ab}v_{cd}^{\dagger }. \label{eq:reduction_rank_one_expansion} \end{align}

No orthonormality or spanning condition on the endpoint matrices is needed.

Proof ▶

Expand both finite matrix products in \(\mathcal T(M)=(E\otimes F)M(\widetilde E\otimes \widetilde F)^{\dagger }\). The term indexed by \((a,b,c,d)\) at row \((w,x)\) and column \((y,z)\) is \(M_{(a,b),(c,d)}E_{wa}F_{xb} \overline{\widetilde E_{yc}\widetilde F_{zd}}\), which is the corresponding entry of the rank-one summand in (8).

For the actual circular complex Gaussian law, each ambient factor \(U_j^{\mathrm{amb}},V_j^{\mathrm{amb}}\) is matrix-valued integrable; this integrability holds for arbitrary fixed real coefficient weights. If \(k{\gt}0\) and the endpoint weights are nonnegative, both \(\widehat X_{\mathrm{amb}}\) and \(\Delta _{\mathrm{amb}}\) are integrable, entrywise and as matrices, and

\begin{align} \mathbb E\widehat X_{\mathrm{amb}} & =X_{\mathrm{amb}}, \label{eq:reduction_ambient_mean} \\ \mathbb E\Delta _{\mathrm{amb}} & =0. \label{eq:reduction_ambient_centered} \end{align}

These expectation identities hold for arbitrary fixed endpoint maps; the isometry equations govern the separate norm-preservation estimate of Theorem 15.2.2. This is the unbiased replacement in [ Ope26a , Equation (5.6) ] .

Proof ▶

Each factor entry is a finite linear combination of integrable Gaussian coordinates. Finite matrix evaluation gives factor integrability. The transport identities express every ambient source entry as a finite linear combination of coordinate source entries. Integrate these sums and use \(\mathbb E\widehat X=X\) from Theorem 15.1.2. Transport of the target gives (9); subtracting it gives (10). Finite evaluation combines the entry identities into the matrix-valued identities.

17.2 Subnormalized vectors and changing memories

For finite Euclidean spaces, the rectangular outer product has entries \((|u\rangle \langle v|)_{ij}=u_i\overline{v_j}\) and satisfies \(\lVert |u\rangle \langle v|\rVert _1\leq \lVert u\rVert \lVert v\rVert \). For vectors \(v,w\) in the same space,

\begin{align} \lVert |v\rangle \langle v|-|w\rangle \langle w|\rVert _1 & \leq (\lVert v\rVert +\lVert w\rVert )\lVert v-w\rVert . \label{eq:reduction_pure_error} \end{align}

Discarding a finite register preserves this upper bound. In particular, if \(\lVert v\rVert ,\lVert w\rVert \leq 1\), both the full and retained density errors are at most \(2\lVert v-w\rVert \). Zero vectors and zero-dimensional spaces are allowed. This is the vector-to-density estimate preceding [ Ope26a , Equation (5.3) ] .

Proof ▶

The trace pairing is \(\operatorname{tr}((|u\rangle \langle v|)^\dagger U)=\langle u,Uv\rangle \). A test contraction \(U\) and Cauchy–Schwarz bound its absolute value by \(\lVert u\rVert \lVert v\rVert \), so trace-norm duality gives the outer-product bound. Split the density difference as \(|v-w\rangle \langle v|+|w\rangle \langle v-w|\) and use the triangle inequality to obtain (11). Partial-trace contraction gives the retained-density bound. The two upper norm bounds give the factor two.

Let \(D_t\) be finite index sets, and let \(G_t,H_t:\mathbb {C}^{D_t}\to \mathbb {C}^{D_{t+1}}\) be contractions for every \(t\in \mathbb {N}\). Define the chronological prefixes by \(\Pi _G(0)=\mathbb {1}\) and \(\Pi _G(t+1)=G_t\Pi _G(t)\). Each prefix is a contraction, including the identity on an empty space, and

\begin{align} \lVert \Pi _G(n)-\Pi _H(n)\rVert & \leq \sum _{t=0}^{n-1}\lVert G_t-H_t\rVert . \label{eq:reduction_telescoping} \end{align}

A uniform error bound \(\lVert G_t-H_t\rVert \leq \delta \) gives the bound \(n\delta \). More generally, for a finite marked set \(S\subset \mathbb {N}\) and \(\delta \geq 0\), errors at most \(\delta \) on \(S\) and exact equality outside \(S\) give \(\lVert \Pi _G(n)-\Pi _H(n)\rVert \leq |S|\delta \). In a real normed space, if \(\lVert G\rVert \leq 1\) and \(\lVert A-G\rVert \leq \delta \) with \(\delta \geq 0\), then \(\lVert A/(1+\delta )\rVert \leq 1\) and \(\lVert A/(1+\delta )-G\rVert \leq 2\delta \). These are the contraction estimates used before [ Ope26a , Equation (5.3) ] .

Proof ▶

Induct on the prefix length. At the next stage, use

\begin{align} \Pi _G(t+1)-\Pi _H(t+1) & =G_t(\Pi _G(t)-\Pi _H(t))+(G_t-H_t)\Pi _H(t). \label{eq:reduction_telescoping_step} \end{align}

Submultiplicativity and the prefix contraction bound add the next gate error. If the gates agree outside \(S\), only indices in \(S\cap \{ 0,\ldots ,n-1\} \) contribute; their number is at most \(|S|\). For rescaling, \(\lVert A\rVert \leq 1+\delta \) gives the contraction bound. The decomposition \(A/(1+\delta )-G=(A-G)/(1+\delta ) +((1+\delta )^{-1}-1)G\) gives the error bound \(2\delta \).

17.3 Actual readout densities and marked error budgets

Let \(\psi \in \mathbb {C}^{D_0}\) satisfy \(\lVert \psi \rVert \leq 1\), and let \(K:\mathbb {C}^{D_n}\to \mathbb {C}^{P\times E}\) be a common contraction into finite physical and discarded registers. Define the actual final vector and retained density by

\begin{align} v_G & =K\Pi _G(n)\psi , \label{eq:reduction_readout_vector} \\ \rho _G & =\operatorname{Tr}_E(|v_G\rangle \langle v_G|). \label{eq:reduction_readout_density} \end{align}

For arbitrary gate families, \(\lVert v_H-v_G\rVert \leq \lVert \Pi _H(n)-\Pi _G(n)\rVert \). If both families are contractions, \(v_G,v_H\) have norm at most one and

\begin{align} \lVert \rho _H-\rho _G\rVert _1 & \leq 2\lVert \Pi _H(n)-\Pi _G(n)\rVert \leq 2\sum _{t=0}^{n-1}\lVert H_t-G_t\rVert . \label{eq:reduction_readout_error} \end{align}

For \(n=0\) the densities coincide and their trace-norm difference is zero; this equality holds for arbitrary \(\psi ,K,G,H\). These are the actual vector and density estimates used in [ Ope26a , Equation (5.3) ] .

Proof ▶

The common readout gives \(v_H-v_G=K(\Pi _H(n)-\Pi _G(n))\psi \); its contraction bound and \(\lVert \psi \rVert \leq 1\) give the vector estimate. Prefix and readout contraction also give the two upper vector norm bounds. Apply Theorem 17.2.1, then (12). At zero length both prefixes are the same identity, so the actual densities agree.

Let \(G_t\) be contractions on the prescribed changing memory spaces, and let \(\psi ,K\) satisfy the hypotheses of Theorem 17.3.1. Choose a finite marked set \(S\) and \(\delta \geq 0\), with \(\lVert A_t-G_t\rVert \leq \delta \) for every \(t\in S\). Set

\begin{align} H_t & =\begin{cases} A_t/(1+\delta ), & t\in S, \\ G_t, & t\notin S. \end{cases} \label{eq:reduction_marked_gates} \end{align}

For the actual densities of (15),

\begin{align} \lVert \rho _H-\rho _G\rVert _1 & \leq 4|S|\delta . \label{eq:reduction_marked_error} \end{align}

If \(|S|{\gt}0\) and \(\epsilon \geq 0\), the choice \(\delta =\epsilon /(8|S|)\) gives error at most \(\epsilon /2\). For \(S=\varnothing \), \(H=G\) and the density error is exactly zero, for arbitrary input, readout, and rescaling parameter. Unmarked private stages remain exact. If all stages are approximated with a uniform error bound and \(H_t=A_t/(1+\delta )\) for every \(t\), the corresponding estimate is \(4n\delta \); for \(n{\gt}0\) and \(\epsilon \geq 0\), taking \(\delta =\epsilon /(8n)\) gives the same half-accuracy bound. With the nonprivate gate count \(M=|S|\), this is [ Ope26a , Equation (5.3) ] .

Proof ▶

Rescaling gives \(\lVert H_t\rVert \leq 1\) and \(\lVert H_t-G_t\rVert \leq 2\delta \) on \(S\); outside \(S\) the error is zero. The supported telescoping bound gives prefix error at most \(2|S|\delta \). Formula (16) doubles this to (20). For positive \(|S|\), \(4|S|\epsilon /(8|S|)=\epsilon /2\). The uniform case uses the uniform telescoping estimate in the same way. An empty marked set makes the two chains identical.

17.4 A product reference with private norm loss

Let \(\mathcal H_A,\mathcal H_E\) be finite-dimensional Euclidean spaces, \(\Omega \in \mathcal H_A\) a unit vector, and \(\Xi \in \mathcal H_E\) with \(\lVert \Xi \rVert \leq 1\). For \(\Psi \in \mathcal H_A\otimes \mathcal H_E\) with \(\lVert \Psi \rVert \leq 1\), write \(\rho _\Psi =\operatorname{Tr}_E|\Psi \rangle \langle \Psi |\). Then

\begin{align} \operatorname{Tr}_E|\Omega \otimes \Xi \rangle \langle \Omega \otimes \Xi | & =\lVert \Xi \rVert ^2|\Omega \rangle \langle \Omega |,\\ \lVert \rho _\Psi -|\Omega \rangle \langle \Omega |\rVert _1 & \leq 2\lVert \Psi -\Omega \otimes \Xi \rVert +1-\lVert \Xi \rVert ^2. \end{align}

In particular, if \(\lVert \Psi -\Omega \otimes \Xi \rVert \leq \varepsilon \) and \(1-\delta \leq \lVert \Xi \rVert \), the density error is at most \(2\varepsilon +2\delta \). Neither \(\Xi \ne 0\) nor \(\delta \leq 1\) is required. These estimates give the explicit constants in the passage from the product reference to the physical density in [ Ope26a , Section 8.1 ] .

Proof ▶

Taking the partial trace of the product density gives the first identity. Its difference from \(|\Omega \rangle \langle \Omega |\) has trace norm \(1-\lVert \Xi \rVert ^2\). Both joint vectors have norm at most one, so Theorem 17.2.1 bounds the density difference between \(\Psi \) and \(\Omega \otimes \Xi \) by twice their vector difference. The triangle inequality gives the stated bound. Finally, for \(s=\lVert \Xi \rVert \), one has \(1-s^2\leq 2(1-s)\leq 2\delta \).

Under the preceding hypotheses,

\begin{align} \lVert \Psi \rVert & \geq 1-\delta -\varepsilon ,\\ 1-\operatorname{tr}\rho _\Psi & \leq 2(\delta +\varepsilon ). \end{align}

These two inequalities remain true without the upper bounds on \(\lVert \Psi \rVert \) and \(\lVert \Xi \rVert \); the second is then a signed trace-deficit bound. If an arbitrary operator \(\sigma \) satisfies \(\lVert \sigma -\rho _\Psi \rVert _1\leq \kappa \), then, with the original upper norm bounds,

\begin{align} \lVert \sigma -|\Omega \rangle \langle \Omega |\rVert _1 & \leq \kappa +2\varepsilon +2\delta . \end{align}

Positivity and Hermiticity of \(\sigma \) are unnecessary. This is the error accumulation in [ Ope26a , Section 8.1 ] .

Proof ▶

The reverse triangle inequality gives the norm bound. Since \(\operatorname{tr}\rho _\Psi =\lVert \Psi \rVert ^2\), the inequality \(1-t^2\leq 2(1-t)\) gives the trace-deficit bound even when \(1-\delta -\varepsilon \) is negative. The last estimate is the triangle inequality through the density of the same joint vector \(\Psi \).

Lemma 17.4.3 Independence of finite coordinate enumerations
#

Independent bijections of the finite row and column coordinates of a complex matrix preserve its nuclear norm, including when either coordinate set is empty.

Proof ▶

The two coordinate changes are isometries. Multiplication by their matrices on the left and right preserves all singular values and hence their sum.