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Lecture notes in computer science

Worst Case Analysis of Non-local Games

Andris Ambainis, Artūrs Bačkurs, Kaspars Balodis, Agnis Škuškovniks, Juris Smotrovs, Madars Virza

Published Jan 1, 2013• 2 citations
Publisher
DOI
10.1007/978-3-642-35843-2_12

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Worst case analysis of non-local games

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We prove a direct product theorem for the one-way entanglement-assisted quantum communication complexity of a general relation $f\subseteq\mathcal{X}\times\mathcal{Y}\times\mathcal{Z}$. For any $\varepsilon, ζ> 0$ and any $k\geq1$, we show that \[ \mathrm{Q}^1_{1-(1-\varepsilon)^{Ω(ζ^6k/\log|\mathcal{Z}|)}}(f^k) = Ω\left(k\left(ζ^5\cdot\mathrm{Q}^1_{\varepsilon + 12ζ}(f) - \log\log(1/ζ)\right)\right),\] where $\mathrm{Q}^1_{\varepsilon}(f)$ represents the one-way entanglement-assisted quantum communication complexity of $f$ with worst-case error $\varepsilon$ and $f^k$ denotes $k$ parallel instances of $f$. As far as we are aware, this is the first direct product theorem for quantum communication. Our techniques are inspired by the parallel repetition theorems for the entangled value of two-player non-local games, under product distributions due to Jain, Pereszlényi and Yao, and under anchored distributions due to Bavarian, Vidick and Yuen, as well as message-compression for quantum protocols due to Jain, Radhakrishnan and Sen. Our techniques also work for entangled non-local games which have input distributions anchored on any one side. In particular, we show that for any game $G = (q, \mathcal{X}\times\mathcal{Y}, \mathcal{A}\times\mathcal{B}, \mathsf{V})$ where $q$ is a distribution on $\mathcal{X}\times\mathcal{Y}$ anchored on any one side with anchoring probability $ζ$, then \[ ω^*(G^k) = \left(1 - (1-ω^*(G))^5\right)^{Ω\left(\frac{ζ^2 k}{\log(|\mathcal{A}|\cdot|\mathcal{B}|)}\right)}\] where $ω^*(G)$ represents the entangled value of the game $G$. This is a generalization of the result of Bavarian, Vidick and Yuen, who proved a parallel repetition theorem for games anchored on both sides, and potentially a simplification of their proof.

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Additive non-Gaussian noise attacks on the scalar Costa scheme (SCS)

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The additive attack public mutual information game is explicitly solved for one of the simplest quantization based watermarking schemes, the scalar Costa scheme (SCS). It is a zero-sum game played between the embedder and the attacker, and the payoff function is the mutual information. The solution of the game, a subgame perfect nash equilibrium, is found by backward induction. Therefore, the Blahut-Arimoto algorithm is employed for numerically optimizing the mutual information over noise distributions. Although the worst case distribution is in general strongly non-Gaussian, the capacity degradation compared to a suboptimal Gaussian noise attack is quite small. The loss, if the embedder optimizes SCS for a Gaussian attack but the worst case attack is employed, is negligible.

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Zero-sum games

Physics World•Apr 1, 1999•0 citations
Non-local games are studied in quantum information because they provide a simple way for proving the difference between the classical world and the quantum world. A non-local game is a cooperative game played by 2 or more players against a referee. The players cannot communicate but may share common random bits or a common quantum state. A referee sends an input xix_ixi​ to the ithi^{th}ith player who then responds by sending an answer aia_iai​ to the referee. The players win if the answers aia_iai​ satisfy a condition that may depend on the inputs xix_ixi​. Typically, non-local games are studied in a framework where the referee picks the inputs from a known probability distribution. We initiate the study of non-local games in a worst-case scenario when the referee's probability distribution is unknown and study several non-local games in this scenario.
We prove a direct product theorem for the one-way entanglement-assisted quantum communication complexity of a general relation f⊆X×Y×Zf\subseteq\mathcal{X}\times\mathcal{Y}\times\mathcal{Z}f⊆X×Y×Z. For any ε,ζ>0\varepsilon, ζ> 0ε,ζ>0 and any k≥1k\geq1k≥1, we show that Q1−(1−ε)Ω(ζ6k/log⁡∣Z∣)1(fk)=Ω(k(ζ5⋅Qε+12ζ1(f)−log⁡log⁡(1/ζ))), \mathrm{Q}^1_{1-(1-\varepsilon)^{Ω(ζ^6k/\log|\mathcal{Z}|)}}(f^k) = Ω\left(k\left(ζ^5\cdot\mathrm{Q}^1_{\varepsilon + 12ζ}(f) - \log\log(1/ζ)\right)\right),Q1−(1−ε)Ω(ζ6k/log∣Z∣)1​(fk)=Ω(k(ζ5⋅Qε+12ζ1​(f)−loglog(1/ζ))), where Qε1(f)\mathrm{Q}^1_{\varepsilon}(f)Qε1​(f) represents the one-way entanglement-assisted quantum communication complexity of fff with worst-case error ε\varepsilonε and fkf^kfk denotes kkk parallel instances of fff. As far as we are aware, this is the first direct product theorem for quantum communication. Our techniques are inspired by the parallel repetition theorems for the entangled value of two-player non-local games, under product distributions due to Jain, Pereszlényi and Yao, and under anchored distributions due to Bavarian, Vidick and Yuen, as well as message-compression for quantum protocols due to Jain, Radhakrishnan and Sen. Our techniques also work for entangled non-local games which have input distributions anchored on any one side. In particular, we show that for any game G=(q,X×Y,A×B,V)G = (q, \mathcal{X}\times\mathcal{Y}, \mathcal{A}\times\mathcal{B}, \mathsf{V})G=(q,X×Y,A×B,V) where qqq is a distribution on X×Y\mathcal{X}\times\mathcal{Y}X×Y anchored on any one side with anchoring probability ζζζ, then ω∗(Gk)=(1−(1−ω∗(G))5)Ω(ζ2klog⁡(∣A∣⋅∣B∣)) ω^*(G^k) = \left(1 - (1-ω^*(G))^5\right)^{Ω\left(\frac{ζ^2 k}{\log(|\mathcal{A}|\cdot|\mathcal{B}|)}\right)}ω∗(Gk)=(1−(1−ω∗(G))5)Ω(log(∣A∣⋅∣B∣)ζ2k​) where ω∗(G)ω^*(G)ω∗(G) represents the entangled value of the game GGG. This is a generalization of the result of Bavarian, Vidick and Yuen, who proved a parallel repetition theorem for games anchored on both sides, and potentially a simplification of their proof.
The additive attack public mutual information game is explicitly solved for one of the simplest quantization based watermarking schemes, the scalar Costa scheme (SCS). It is a zero-sum game played between the embedder and the attacker, and the payoff function is the mutual information. The solution of the game, a subgame perfect nash equilibrium, is found by backward induction. Therefore, the Blahut-Arimoto algorithm is employed for numerically optimizing the mutual information over noise distributions. Although the worst case distribution is in general strongly non-Gaussian, the capacity degradation compared to a suboptimal Gaussian noise attack is quite small. The loss, if the embedder optimizes SCS for a Gaussian attack but the worst case attack is employed, is negligible.