A dual formula for the noncommutative transport distance
Abstract.
In this article we study the noncommutative transport distance introduced by Carlen and Maas and its entropic regularization defined by Becker and Li. We prove a duality formula that can be understood as a quantum version of the dual Benamou–Brenier formulation of the Wasserstein distance in terms of subsolutions of the Hamilton–Jacobi–Bellmann equation.
Introduction
The theory of optimal transport [Vil03, Vil09] has experienced rapid growth in recent years with applications in diverse fields across pure and applied mathematics. Along with this growth came a lot of interest in extending the methods of optimal transport beyond the scope of its original formulation as an optimization problem for the transport cost between two probability measures.
One such extension deals with “quantum spaces”, where the probability measures are replaced by density matrices or density operators. Most of the work dealing with quantum optimal transport in this sense can be grouped into one of the following two categories. The first approach (see e.g. [CM14, CM17, MM17, CGT18, RD19, BV20, CM20]) relies on a noncommutative analog of the Benamou–Brenier formulation [BB00] of the Wasserstein distance for probability measures on Euclidean space
This approach has proven fruitful in applications to noncommutative functional inequalities, similar in spirit to the heuristics known as Otto calculus [CM17, CM20, DR20, WZ20].
The second approach (see e.g. [NGT15, GMP16, Pey+19, Duv20, Pal+20, DPT21]) seeks to find a suitable noncommutative analog of the Monge–Kantorovich formulation [Kan42] of the Wasserstein distance via couplings (or transport plans):
This approach also allows to consider a quantum version of Monge–Kantorovich problem for arbitrary cost functions. So far, possible connections between these two approaches in the quantum world stay elusive.
The focus of this article lies on the noncommutative transport distance introduced in the first approach. More precisely, we prove a dual formula that is a noncommutative analog of the expression of the classical -Wasserstein distance in terms of subsolutions of the Hamilton–Jacobi equation [OV00, BGL01]
This result yields a noncommutative version of the dual formula obtained independently by Erbar, Maas and the author [EMW19] and Gangbo, Li and Mou [GLM19] for the Wasserstein-like transport distance on graphs. In fact, we prove a dual formula that is not only valid for the metric , but also for the entropic regularization recently introduced by Becker–Li [BL21].
With the notation introduced in the next section, the main result of this article reads as follows.
Theorem.
Let be an invertible density matrix and an ergodic quantum Markov semigroup on that satisfies the -DBC. The entropic regularization of noncommutative transport distance induced by satisfies the following dual formula:
Here stands for the set of all Hamilton–Jacobi–Bellmann subsolutions, a suitable noncommutative variant of solutions of the differential inequality
Other metrics similar to also occur in the literature, most notably the one called the “anticommutator case” in [CGT18, Che+20, BL21]. In [Wir18, CM20], a class of such metrics was studied in a systematic way, and our main theorem applies in fact to this wider class of metrics. For the anticommutator case, this duality formula was obtained before in [Che+20].
There are still some very natural questions left open. For one, we do not discuss the existence of optimizers. While for the primal problem this follows from a standard compactness argument, this question is more delicate for the dual problem, even when dealing with probability densities on discrete spaces instead of density matrices, and one has to relax the problem to obtain maximizers (see [GLM19, Sections 6–7]).
Another interesting direction would be to extend the duality result from matrix algebras to infinite-dimensional systems. While a definition of the metric for quantum Markov semigroups on semi-finite von Neumann algebras is available [Hor18, Wir18], the problem of duality seems to be much harder to address. Even for abstract diffusion semigroups, the best known result only shows that the primal distance is the upper length distance associated with the dual distance and leaves the question of equality open [AES16, Proposition 10.11].
Acknowledgments
The author wants to thank Jan Maas for helpful comments. He acknowledges support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716117) and from the Austrian Science Fund (FWF) through project F65.
1. Setting and basic definitions
In this section we introduce basic facts and definitions about quantum Markov semigroups that will be used later on. In particular, we review the definition of the noncommutative transport distance from [CM17] and its entropic regularization introduced in [BL21]. Our notation mostly follows [CM17, CM20].
Let denote the complex matrices and let be a unital -subalgebra of . Let denote the self-adjoint part of . We write for the normalized trace on and for the Hilbert space formed by equipping with the GNS inner product
The adjoint of a linear operator is denoted by .
We write for the set of all density matrices on , that is, all positive elements with . The subset of invertible density matrices is denoted by .
A quantum Markov semigroup (QMS) on is a family of linear operators on that satisfy the following conditions:
-
•
is unital and completely positive for every ,
-
•
, for all ,
-
•
is continuous.
We consider a QMS on which extends to a QMS on satisfying the -DBC for some density matrix , that is,
for . Let denote the generator of , that is, the linear operator on given by
We further assume that is ergodic (or primitive), that is, the kernel of is one-dimensional.
By Alicki’s theorem [Ali76, Theorem 3], [CM17, Theorem 3.1] there exists a finite set , real numbers for and for with the following properties:
-
•
for ,
-
•
for ,
-
•
,
-
•
for
such that
for .
The numbers are called Bohr frequencies of and are uniquely determined by .
The matrices are not uniquely determined by and , but in the following we will fix a set that satisfies the preceding conditions.
Let
where is a copy of for . We write for and
We write for the adjoint of , that is,
For and define
Given , we define
Now let with the Bohr frequencies of . For we write for the set of all pairs such that with , , and
for a.e. .
We define a metric on by
A standard mollification argument shows that the infimum can equivalently be taken over with .
For , this is the noncommutative transport distance introduced in [CM17] (as distance function associated with a Riemannian metric on ), and for , this is the entropic regularization of introduced in [BL21].
By a substitution one can reformulate the minimization problem for in such a way that the constraint becomes independent from . For that purpose define the relative entropy of with respect to by
and the Fisher information of by
According to [BL21, Theorem 1], one has
The metric is intimately connected to the relative entropy and therefore well-suited to study its decay properties along the quantum Markov semigroup. For other applications, variants of the metric have also proven useful (e.g. [CGT18, Che+20]), for which the operator is replaced. A systematic framework of these metrics has been developed in [Wir18, CM20]. It can be conveniently phrased in terms of so-called operator connections.
Let be an infinite-dimensional Hilbert space. A map is called an operator connection [KA80] if
-
•
and imply ,
-
•
,
-
•
, imply .
For example, for every the map
is an operator connection.
It can be shown that every operator connection satisfies
for and unitary [KA80, Section 2]. Embedding into , one can view as bounded linear operators on , and the unitary invariance of ensures that does not depend on the embedding of into .
For define
With this notation we can write
Since and commute, we have
() |
for and , where are the eigenvalues of and the corresponding spectral projections.
More generally let be a family of operator connections and define
Then one can define a distance by
.
If as above, then we retain the original metric , while for (and ) one obtains the distance studied in [CGT18, Che+20].
Later we will make the additional assumption that . It follows from the representation theorem of operator means [KA80] that the class of metrics with subject to this symmetry condition is exactly the class of metrics satisfying Assumptions 7.2 and 9.5 in [CM20].
For technical reasons, it can be useful to allow for curves of density matrices that are not necessarily invertible. For this purpose, we make the following convention: If is a positive operator and , we define
Since and is injective on , the element in this definition exists and is unique. Moreover, this convention is clearly consistent with the usual definition if is invertible.
Alternatively, as a direct consequence of the spectral theorem, can be expressed as
where are the eigenvalues of and an orthonormal basis of corresponding eigenvectors.
Lemma 1.1.
If , , are positive invertible operators that converge monotonically decreasing to , then
for all .
Proof.
From the spectral expression it is easy to see that
and the same for replaced by . Moreover, since , we have . Thus
Since is monotonically increasing, this settles the claim. ∎
Write for the set of all pairs such that with , , and
for a.e. . The only difference to the definition of is that is not assumed to be invertible.
Proposition 1.2.
For we have
Proof.
It suffices to show that every curve can be approximated by curves in such that the action integrals converge.
For that purpose let
Since is assumed to be ergodic, for there exists such that
and
with a constant (depending only on the spectral gap of ). In particular, it is bounded independent of .
Moreover, if is the smallest eigenvalue of , which is strictly positive by assumption, then .
Thus
as . Similarly one can show
By the same argument as above, for a.e. there exists a unique gradient such that
and
Since , the norm on the right side is bounded independent of , so that
with a constant independent of . As for , this implies
as .
With
we have
Furthermore,
where we used the substitution in the last step.
By Lemma 1.1 and the monotone convergence theorem we obtain
Together with the convergence result for from above, this implies
Altogether we have shown
2. Real subspaces
Since the proof of the main result relies on convex analysis methods for real Banach spaces, we need to identify suitable real subspaces for our purposes. For this is simply , but for this is less obvious and will be done in the following.
For denote by the unique index in such that . Let be the linear span of , and define a linear map by
By the product rule, also belongs to and .
Lemma 2.1.
The map is anti-unitary.
Proof.
For we have
Let
By the previous lemma, is a real Hilbert space.
Lemma 2.2.
Let be a family of operator connections such that for all . If and , then .
Proof.
For the statement follows directly from the definitions. For first note that
as a consequence of the spectral representation ( ‣ 1).
Thus
3. Duality
In this section we prove the duality theorem announced in the introduction. Our strategy follows the same lines as the proof in the commutative case in [EMW19]. It crucially relies on the Rockefellar–Fenchel duality theorem quoted below. Throughout this section we fix a quantum Markov semigroup with generator satisfying the -DBC for some and a family of operator connections such that for all .
We need the following definition for the constraint of the dual problem. Here and in the following we write
for and .
Definition 3.1.
A function is said to be a Hamilton–Jacobi–Bellmann subsolution if for a.e. we have
The set of all Hamilton–Jacobi–Bellmann subsolutions is denoted by .
Our proof will establish equality between the primal and dual problem, but before we begin, let us show that one inequality is actually quite easy to obtain.
Proposition 3.2.
For all we have
Proof.
For and we have
where we used and for the first inequality and Young’s inequality for the second inequality. ∎
To prove actual equality, our crucial tool is the Rockefellar–Fenchel duality theorem (see e.g. [Vil03, Theorem 1.9], which we quote here for the convenience of the reader. Recall that if is a (real) normed space, the Legendre–Fenchel transform of a proper convex function is defined by
Theorem 3.3.
Let be a real normed space and proper convex functions with Legendre–Fenchel transforms . If there exists such is continuous at and , then
Before we state the main result, we still need the following useful inequality.
Lemma 3.4.
For any operator connection the map
is smooth and its Fréchet derivative satisfies
for with equality if .
Proof.
Smoothness of is a consequence of the representation theorem of operator connections [Theorem 3.4][KA80]. For the claim about the Fréchet derivative first note that is concave [KA80, Theorem 3.5]. Therefore for all and by [Han97, Proposition 2.2].
The fundamental theorem of calculus implies
Since is -homogeneous, its derivative is -homogeneous. Thus, if we replace by and let , we obtain
Moreover, the -homogeneity of implies , which settles the claim. ∎
Theorem 3.5 (Duality formula).
For we have
Proof.
The second inequality follows easily by mollifying. We will show the duality formula for Hamilton–Jacobi subsolutions in . For this purpose we use the Rockefellar–Fenchel duality formula from Theorem 3.3.
Let be the real Banach space
By the theory of linear ordinary differential equations, the map
is a linear isomorphism.
Thus the dual space can be isomorphically identified with
via the dual pairing
Define functionals by
Here
It is easy to see that and are convex. Moreover, for and we have , hence , and
for all , hence . Furthermore, is clearly continuous at .
Moreover,
Let us calculate the Legendre transforms of and , keeping in mind the identification of . For we obtain
Since the last expression is homogeneous in , we have unless
for .
This implies and and
Thus
Here denotes the set of all pairs satisfying , and
The difference to the definitions of (or ) and is that we do not make any positivity or normalization constraints. Note however that if , then
so that (and ).
Now let us turn to the Legendre transform of . We have
Since implies for all , we have unless . Moreover, it follows from the definition of that unless for a.e. .
For we have
We will show next that the inequalities are in fact equalities. Let and . Moreover, let with the notation from Lemma 3.4. Since
is a bounded linear map that depends continuously on , there exists a unique continuous map such that
for every and .
Let
We claim that . Indeed,
where the inequality follows from Lemma 3.4. Note that we have equality for .
In particular, for we obtain
On the other hand,
where we again used Lemma 3.4 for the first inequality.
Put together, we have
and
follows from the monotone convergence theorem.
Hence
if for a.e. . Together with the formula for , we obtain
where the last equality follows from Proposition 1.2.
An application of the Rockefellar–Fenchel theorem yields the desired conclusion. ∎
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