Algorithms, Bounds, and Strategies for Entangled XOR Games

01/02/2018
by   Adam Bene Watts, et al.
0

We study the complexity of computing the commuting-operator value ω^* of entangled XOR games with any number of players. We introduce necessary and sufficient criteria for an XOR game to have ω^* = 1, and use these criteria to derive the following results: 1. An algorithm for symmetric games that decides in polynomial time whether ω^* = 1 or ω^* < 1, a task that was not previously known to be decidable, together with a simple tensor-product strategy that achieves value 1 in the former case. The only previous candidate algorithm for this problem was the Navascués-Pironio-Acín (also known as noncommutative Sum of Squares or ncSoS) hierarchy, but no convergence bounds were known. 2. A family of games with three players and with ω^* < 1, where it takes doubly exponential time for the ncSoS algorithm to witness this (in contrast with our algorithm which runs in polynomial time). 3. A family of games achieving a bias difference 2(ω^* - ω) arbitrarily close to the maximum possible value of 1 (and as a consequence, achieving an unbounded bias ratio), answering an open question of Briët and Vidick. 4. Existence of an unsatisfiable phase for random (non-symmetric) XOR games: that is, we show that there exists a constant C_k^unsat depending only on the number k of players, such that a random k-XOR game over an alphabet of size n has ω^* < 1 with high probability when the number of clauses is above C_k^unsat n. 5. A lower bound of Ω(n (n)/(n)) on the number of levels in the ncSoS hierarchy required to detect unsatisfiability for most random 3-XOR games. This is in contrast with the classical case where the n-th level of the sum-of-squares hierarchy is equivalent to brute-force enumeration of all possible solutions.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro