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arXiv:1311.3364

From Consensus to Robust Randomized Algorithms: A Symmetrization Approach ∗

Luca Mazzarella, Francesco Ticozzi, Alain Sarlette

Published Nov 14, 2013• 14 citations
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Abstract

This paper interprets and generalizes consensus-type algorithms as switching dynamics leading to symmetrization with respect to the actions of a finite group. Explicit convergence results are provided in a grouptheoretic formulation, both for deterministic and for stochastic dynamics. We show how the symmetrization framework directly extends the scope of consensustype algorithms and results to applications as diverse as consensus on probability distributions (either classical or quantum), computation of the discrete Fourier transform, uniform random state generation, and openloop disturbance rejection by quantum dynamical decoupling. This indicates a way to extend the desirable robustness of consensus-inspired algorithms to even more fields of application.

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This paper interprets and generalizes consensus-type algorithms as switching dynamics leading to symmetrization with respect to the actions of a finite group. Explicit convergence results are provided in a grouptheoretic formulation, both for deterministic and for stochastic dynamics. We show how the symmetrization framework directly extends the scope of consensustype algorithms and results to applications as diverse as consensus on probability distributions (either classical or quantum), computation of the discrete Fourier transform, uniform random state generation, and openloop disturbance rejection by quantum dynamical decoupling. This indicates a way to extend the desirable robustness of consensus-inspired algorithms to even more fields of application.
This paper extends the gossip algorithm, widely studied in the literature on distributed computing and control algorithms, to networks of quantum systems. In doing so, we reinterpret the classical algorithm and the average consensus task as a symmetrization problem with respect to the action of the permutation group. This allows us to extend in a natural way the gossip consensus algorithm to the quantum setting and prove its convergence properties to symmetric states while preserving the expectation of permutation-invariant global observables.
Abstract—This paper extends the consensus framework, widely studied in the literature on distributed computing and control algorithms, to networks of quantum systems. We define consensus situations on the basis of invariance and symmetry properties, finding four different generalizations of classical consensus states. This new viewpoint can be directly used to study consensus for probability distributions, as these can be seen as a particular case of quantum statistical states: in this light, our analysis is also relevant for classical problems. We then extend the gossip consen-sus algorithm to the quantum setting and prove it converges to symmetric states while preserving the expectation of permutation-invariant global observables. Applications of the framework and the algorithms to estimation and control problems on quantum networks are discussed. Index Terms—Consensus, distributed algorithms, Markov pro-cesses, quantum information and control. I.
This paper extends the consensus framework, widely studied in the literature on distributed computing and control algorithms, to networks of quantum systems. We define consensus situations on the basis of invariance and symmetry properties, finding four different generalizations of classical consensus states. This new viewpoint can be directly used to study consensus for probability distributions, as these can be seen as a particular case of quantum statistical states: in this light, our analysis is also relevant for classical problems. We then extend the gossip consensus algorithm to the quantum setting and prove it converges to symmetric states while preserving the expectation of permutation-invariant global observables. Applications of the framework and the algorithms to estimation and control problems on quantum networks are discussed.
This paper presents a proof-of-concept for a novel consensus algorithm inspired by principles from quantum mechanics. The core idea is to leverage concepts such as superposition and entanglement to potentially improve the convergence speed and robustness of distributed consensus protocols. Unlike existing approaches that merely mimic quantum computation, our algorithm directly translates quantum mechanical ideas into an algorithmic framework. Specifically, the algorithm employs a probabilistic superposition of potential consensus states, guided by entanglement-like correlations between participating nodes. This approach aims to accelerate the process of reaching a common agreement within the distributed system. The theoretical framework is outlined, and the key mathematical formulations are presented. This document serves as the initial step in exploring this potentially transformative direction in distributed consensus.
This paper explores the potential of quantum-inspired algorithms for enhancing distributed consensus protocols. Traditional distributed consensus algorithms face challenges in scalability, efficiency, and robustness, particularly in large-scale systems. We propose a novel approach that draws inspiration from quantum superposition and entanglement to design a more effective consensus mechanism. The core idea involves simulating a superposition of potential agreement states and exploiting entanglement-like correlations to accelerate convergence. This approach aims to overcome limitations inherent in classical algorithms, offering a pathway to improved performance in distributed systems. The theoretical framework and key components of the proposed algorithm are detailed, including the mathematical formulation of the superposition state and the convergence analysis. The primary goal is to demonstrate a fundamental shift in the design paradigm, moving beyond purely classical approaches and leveraging concepts from quantum mechanics for enhanced distributed consensus.
This paper investigates the application of quantum-inspired measurement techniques to enhance distributed consensus algorithms. Traditional distributed consensus protocols often suffer from slow convergence rates and vulnerability to network disruptions. We propose a novel approach that leverages principles of quantum measurement—specifically, the concept of superposition and selective observation—to improve consensus performance. Our method involves a probabilistic sampling strategy where nodes selectively measure the state of their neighbors, guided by a carefully designed measurement operator. This reduces redundant communication, accelerates convergence, and increases robustness against malicious or faulty nodes. We formally define the consensus protocol, provide a mathematical analysis of its convergence properties, and demonstrate, through theoretical arguments, the potential for significant improvements in key metrics such as convergence time and fault tolerance. The core contribution lies in translating quantum-inspired ideas into a practical distributed consensus framework, offering a path toward more efficient and reliable distributed systems.