Universal Gaps for XOR Games from Estimates on Tensor Norm Ratios



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Aubrun, Guillaume and Lami, Ludovico and Palazuelos, Carlos and Szarek, Stanisław J. and Winter, Andreas (2020) Universal Gaps for XOR Games from Estimates on Tensor Norm Ratios. Communications in Mathematical Physics, 375 (1). pp. 679-724. ISSN 0010-3616

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Official URL: https://doi.org/10.1007/s00220-020-03688-2


We define and study XOR games in the framework of general probabilistic theories, which encompasses all physical models whose predictive power obeys minimal requirements. The bias of an XOR game under local or global strategies is shown to be given by a certain injective or projective tensor norm, respectively. The intrinsic (i.e. model-independent) advantage of global over local strategies is thus connected to a universal function r(n, m) called ‘projective–injective ratio’. This is defined as the minimal constant ρ such that ∥⋅∥X⊗πY⩽ρ∥⋅∥X⊗εY holds for all Banach spaces of dimensions dimX=n and dimY=m, where X⊗πY and X⊗εY are the projective and injective tensor products. By requiring that X=Y, one obtains a symmetrised version of the above ratio, denoted by rs(n). We prove that r(n,m)⩾19/18 for all n,m⩾2, implying that injective and projective tensor products are never isometric. We then study the asymptotic behaviour of r(n, m) and rs(n), showing that, up to log factors: rs(n) is of the order n−−√ (which is sharp); r(n, n) is at least of the order n1/6; and r(n, m) grows at least as min{n,m}1/8. These results constitute our main contribution to the theory of tensor norms. In our proof, a crucial role is played by an ‘ℓ1/ℓ2/ℓ∞ trichotomy theorem’ based on ideas by Pisier, Rudelson, Szarek, and Tomczak-Jaegermann. The main operational consequence we draw is that there is a universal gap between local and global strategies in general XOR games, and that this grows as a power of the minimal local dimension. In the quantum case, we are able to determine this gap up to universal constants. As a corollary, we obtain an improved bound on the scaling of the maximal quantum data hiding efficiency against local measurements.

Item Type:Article
Subjects:Sciences > Mathematics > Mathematical analysis
ID Code:77288
Deposited On:12 Apr 2023 11:22
Last Modified:12 Apr 2023 11:24

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