AI 中文总结
研究相互作用开放量子系统非平衡动力学模拟难题，提出神经规范-P表示法，通过神经网络参数化随机规范并利用精确矩方程残差优化。用于驱动耗散玻色-哈伯德模型，长时间演化时该表示法保持准确，展现其模拟非平衡开放量子多体动力学的潜力。
AI 中文摘要
模拟相互作用的开放量子系统的非平衡动力学在超出小系统规模时仍然具有挑战性。量子相空间表示提供了一种可扩展的方法，但其有用的模拟时间可能受到广泛分布尾部和相关边界项的限制。我们引入了用于开放玻色子系统的神经规范-P表示，其中随机规范由神经网络参数化，并使用精确矩方程残差进行优化。对于单格点和方格点设置下的驱动耗散玻色-哈伯德模型，神经规范-P表示在向稳态的长时间演化过程中保持准确，而相应的无规范表示在更早的时间就变得不可靠。这些结果证明了神经规范-P表示在精确模拟非平衡开放量子多体动力学方面的潜力。
英文摘要
Simulating the nonequilibrium dynamics of interacting open quantum systems remains challenging beyond small system sizes. Quantum phase-space representations provide a scalable approach, but their useful simulation time can be limited by broad distribution tails and the associated boundary terms. We introduce the neural gauge-$P$ representation for open bosonic systems, in which stochastic gauges are parameterized by neural networks and optimized using exact moment equation residuals. For the driven-dissipative Bose--Hubbard model in both single-site and square-lattice settings, the neural gauge-$P$ representation remains accurate during long-time evolution toward the steady state, whereas the corresponding ungauged representation becomes unreliable at substantially earlier times. These results demonstrate the potential of the neural gauge-$P$ representation for accurate simulations of nonequilibrium open quantum many-body dynamics.
