Proof of Tensor Powers Scale the Wasserstein Distance by the Square Root of N, and the One-Particle Marginal is Lipschitz with Constant
lemmalem:tensor-marginal-wasserstein-2026aAveraging the block images of an optimal coupling of P and P' gives a coupling of the one-particle marginals with cost 1/N times the original, and pushing the tensor power of an optimal coupling of the one-particle measures forward by the pair of product projections gives a coupling of the tensor powers with cost N times the original.
Each result cited is universally quantified over the data in its own statement. For , couplings , the quadratic cost and are those of Couplings of Two Probability Measures on Euclidean Space and Their Quadratic Cost §coupling, Couplings of Two Probability Measures on Euclidean Space and Their Quadratic Cost §cost and The Quadratic Wasserstein Distance on Euclidean Space §distance read with in place of (Wasserstein Spaces, Random Vectors, Vector Fields and Symmetric Matrices in Every Dimension: Standing Notation §dimensions), are the coordinate projections of Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §projections, and on is Borel and nonnegative by Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §functions, so that . The block maps of are written , and those of , namely Particle Blocks of the Configuration Space: Block Maps, Configurations, Product Maps and Diagonal Points §blocks read with in place of , are written ; the sibling results Particle Blocks of the Configuration Space: Block Maps, Configurations, Product Maps and Diagonal Points, Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts, Existence and Uniqueness of Tensor Powers, and the Average of the Block Marginals, The One-Particle Marginal of a Probability Measure on the Configuration Space and Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts are applied with in place of where these maps occur. Composites of Borel maps are Borel by Probability Measures on Euclidean Space and Random Vectors: Standing Notation §borel-maps, and push-forwards and their change of variables are those of Probability Measures on Euclidean Space and Random Vectors: Standing Notation §pushforward.
Step 1 (Claim 1). Let ; then by Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §moments. By Existence of an Optimal Coupling of Two Probability Measures with Finite Second Moment (with ) there is with . For let , Borel by Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §linear and Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §pairing; by Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §projections, for . Let send to the configuration of Particle Blocks of the Configuration Space: Block Maps, Configurations, Product Maps and Diagonal Points §configuration, so that . Each component of is a component of some (Block Indices: Enumerating an Initial Segment of Length qN by Blocks and Positions §bijection), hence Borel, so is Borel by claims 2 and 5 of The Borel Sigma-Algebra of a Euclidean Space as a Product, and Measurability of Projections, Sequentially Continuous Maps, and Open and Closed Sets. Put and .
For , The One-Particle Marginal of a Probability Measure on the Configuration Space §marginal, , and give
and likewise . So .
For , Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §linear gives , so by Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §inner-product, and . By monotonicity (claim 1 of Linearity and Monotonicity of the Lebesgue Integral), , so each nonnegative Borel is integrable with respect to . Hence, by the identity of Existence and Uniqueness of Tensor Powers, and the Average of the Block Marginals §average for and , change of variables for , and Linearity of the Lebesgue Integral over a Finite Sum of Integrable Functions §linear,
By The Quadratic Wasserstein Distance on Euclidean Space §distance, ; multiplying by gives claim 1.
Step 2 (Lower bound in claim 2). Let . Then by Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §moments, and , by Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §marginal-of-tensor. Step 1 gives .
Step 3 (Upper bound in claim 2). By Existence of an Optimal Coupling of Two Probability Measures with Finite Second Moment (with ) there is with . Let be its tensor power (The Tensor Power of a Probability Measure on Euclidean Space §tensor with in place of ), and let be the product maps of Particle Blocks of the Configuration Space: Block Maps, Configurations, Product Maps and Diagonal Points §product-map of the projections (with in place of and in place of ); they are Borel by Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §product-map. The pairing is Borel by Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §pairing, with by Pairs of Euclidean Points: Coordinate Projections, Pairings, the Product Measure on a Euclidean Space, Borel Norm Functions and Finite Sets §projections. Put . For , Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §pushforward and give
and likewise ; so .
For , Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §inner-product, Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §linear and Particle Blocks: Linearity, Splitting of Inner Products, Product Maps and Diagonal Shifts §product-map () give
By Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §particle-laws and change of variables, , so each is integrable with respect to . Hence, by change of variables for , Linearity of the Lebesgue Integral over a Finite Sum of Integrable Functions §linear, the integration identity of The One-Particle Marginal of a Probability Measure on the Configuration Space §marginal and Tensor Powers and One-Particle Marginals: Particle Laws, Product Integrals, Push-Forwards, Moments, Product Maps and Diagonal Shifts §marginal-of-tensor,
By The Quadratic Wasserstein Distance on Euclidean Space §distance, . Together with Step 2 and antisymmetry of the order of , this is claim 2.
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