On the Applicability of Bell's Inequality

V. M. González-Robles

Jing‐Ling Chen, Dong-Ling Deng

We develop a systematic approach to establish Bell inequalities for qubits based on the Cauchy-Schwarz inequality. We also use the concept of distinct ``roots'' of the Bell function to classify some well-known Bell inequalities for qubits. As applications of the approach, we present three unique and tight Bell inequalities for four and three qubits, respectively.

N. Glenn Gratke

Hui-Xian Meng, Yu Zhang, Xing-Yan Fan +3 more

It is very difficult and important to construct Bell inequalities for n-partite, k-settings of measurement, and d-dimensional (n,k,d) systems. Inspired by the iteration formula form of the Mermin-Ardehali-Belinski{\uı}-Klyshko (MABK) inequality, we generalize the multi-component correlation functions for bipartite d-dimensional systems to n-partite ones, and construct the corresponding Bell inequality. The Collins-Gisin-Linden-Massar-Popescu inequality can be reproduced by this way. The most important result is that for prime d the general Bell function in full-correlated multi-component correlation function form for (n,2,d) systems can be reformulated in iteration formula by two full-correlated multi-component Bell functions for (n-1,2,d) systems. As applications, we recover the MABK inequality and the most robust coincidence Bell inequalities for (3,2,3),(4,2,3),(5,2,3), and (3,2,5) Bell scenarios with this iteration formula. This implies that the iteration formula is an efficient way of constructing multi-partite Bell inequalities. In addition, we also give some new Bell inequalities with the same robustness but inequivalent to the known ones.

Mario Iannuzzi, Vittorio Merlo

Xinyu Xu, Ping Zhu, Weikang Li +3 more

Exploring the world of Bell inequalities requires numerical methods that scale effectively with the number of parties, inputs, and outputs. Such methods can be used to study large-scale Bell inequalities beyond the reach of analytic methods, to design experiments for violating more complicated Bell inequalities, and to enable applications of Bell inequality violation. Such applications include randomness generation and multi-agent coordination with restricted communication. However, current general purpose optimizers such as the see-saw method struggle to handle Bell inequalities with only a dozen inputs and outputs. We introduce an optimizer for the quantum value of a Bell inequality based on batched gradient descent (BGD). More precisely, our BGD optimizer is a differentiable search over feasible quantum strategies in which states and projective measurements are generated from unconstrained variables. The Bell expression is evaluated by a direct tensor contraction without forming the prohibitively large Bell operator. This formulation makes parallel random restarts inexpensive and is thus naturally implemented by a GPU. We evaluate our optimizer against the see-saw method and find a significant speedup for a wide variety of Bell inequality families. Implemented on a GPU, our BGD optimizer can optimize Bell inequalities in our numerical experiments with more than a thousand inputs or outputs within minutes. For these large-scale Bell inequalities, we also efficiently compute the classical value by enumerating possible strategies using a GPU or expressing the optimization as a mixed-integer linear program. Our optimizers can serve as numerical evaluation tools for Bell experiments and multi-agent coordination problems modeled by Bell inequalities with many inputs and outputs, a common feature of real-world scenarios such as high frequency trading and distributed systems.