Exploring flocking via quantum many-body physics techniques

Giacomo Torlai, Juan Carrasquilla, David J. Schwab +1 more

Yoshio Kuramoto

Byjesh N. Radhakrishnan, Reyhaneh Khasseh, Thomas Schmidt +1 more

Active quantum matter has very recently emerged at the intersection between bio- and quantum many-body physics, combining the self-organization of living systems with the coherence of the quantum world. Active quantum systems have been shown to exhibit flocking - a collective phenomenon with no precedent in equilibrium quantum physics. In this work we uncover an unexpected layer of quantum order in active quantum flocks: they can become superfluid. We show that, in addition to the symmetry breaking associated with their directed motion, these flocks can also break an additional U(1) symmetry, giving rise to off-diagonal long-range order. For a microscopic model of active hard-core bosons governed by Lindblad dynamics, we derive an effective long-wavelength description of the single-particle density matrix and demonstrate that the flocking phase develops an instability toward off-diagonal long-range order characteristic of superfluid behavior. Our findings reveal active quantum matter as a promising research direction for discovering exotic nonequilibrium phases of quantum matter.

Wei-chen Guo, Bao-quan Ai, Liang He

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine architectures that are trained via either an unsupervised approach called learning by confusion or a widely used supervised approach. We find that after the training via either the unsupervised approach or the supervised one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.

Adam G. D’Souza

Jincheng Zhang

This paper explores the potential of algorithmic quantum simulation to unlock insights into complex, emergent phenomena. We investigate the design of an algorithm capable of modeling flocking, convection, and other systems exhibiting self-organization through quantum mechanics. The core claim is that this approach, leveraging quantum algorithms, will provide unprecedented predictive power and allow for the discovery of novel patterns and behaviors within these systems. The paper details the chosen quantum algorithm, its implementation, and preliminary results demonstrating the simulation of a simplified flocking example. We will discuss the challenges and opportunities associated with this research, highlighting the potential impact on fields ranging from materials science to biology.