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Lecture notes in computer science

A Quantum Leap for Fairness: Quantum Bayesian Approach for Fair Decision Making

Ece Çiǧdem Mutlu, Özlem Özmen Garibay

Published Jan 1, 2021• 9 citations
Publisher
DOI
10.1007/978-3-030-90963-5_37

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Abstract In multi-agent systems, Bayesian networks are pivotal for inference and decision-making under uncertainty, yet they face significant challenges, such as high computational complexity and slow decision speed. Quantum computing, leveraging superposition and entanglement, offers potential advantages for solving certain complex problems. Therefore, exploring Bayesian networks within the quantum multi-agent framework promises enhanced inference and decision-making capabilities. While quantum Bayesian networks for inference have been extensively studied, quantum dynamic Bayesian networks for inference and quantum decision networks for decision-making remain underexplored. In the noisy intermediate-scale quantum (NISQ) era, quantum computers struggle to process the long-term temporal structures of dynamic Bayesian networks due to limited resources. To address this, we propose a recursive quantum-classical hybrid Bayesian network inference method, which decomposes dynamic Bayesian networks into smaller subnetworks along the time series. Each subnetwork is represented by a reduced-scale quantum circuit, and the forward and backward operators are computed recursively, enabling efficient filtering and smoothing inference. In addition, we propose an optimal decision-making method based on quantum decision networks, which maps utility values to quantum probabilities and identifies the optimal action by determining the quantum state corresponding to the maximum expected probability. We validate the effectiveness of these algorithms using the IonQ quantum simulator and compare their performance with classical methods. The results demonstrate that our proposed quantum algorithms can effectively perform inference and decision-making tasks well on NISQ devices. The proposed methods provide a foundation for collaborative inference and decision-making within the quantum multi-agent framework.
Interesting connection has been established between two apparently unrelated concepts, namely, quantum nonlocality and Bayesian game theory. It has been shown that nonlocal correlations in the form of advice can outperform the best classical strategies in common interest Bayesian games and can also surpass the fair classical equilibrium payoffs in conflicting games. However a conflicting game may have unfair equilibrium strategies, which are the archetypal of such games and never occur in common interest games. In this work, making the connection between nonlocality and Bayesian game more striking, we show that quantum strategies can outperform even the unfair classical equilibrium strategies in conflicting games. For this purpose here we consider the conflicting game proposed in [Phys. Rev. Lett. 114, 020401 (2015)]. Moreover, we provide a complete characterization of the nonlocal correlations that are advantageous over the best fair and unfair classical strategies in the said game.