基于物理感知潜在初始化的生成式IQP电路学习
Generative IQP Circuit Learning with Physics-Informed Latent Initialization
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中文总结 AI 辅助
本研究针对IQP生成式学习，提出基于PINN替代模型的结构化潜在初始化方案，在伯格斯方程求解任务中，该方案比随机初始化性能更优，可提升量子生成式模型的适配能力与准确率。
中文摘要 AI 辅助
基于瞬时量子多项式时间（IQP）电路的量子生成式学习可受益于高效的经典训练策略。近期一种基于IQP的生成式建模的潜在适应框架，在同一任务的不同超参数实例间共享电路参数，仅为每个新实例适配低维潜在变量。然而现有方法随机初始化该潜在变量，这会限制优化效率与性能。本研究引入一种用于IQP生成式学习的物理感知潜在初始化方案，以改进现有的随机初始化方案。受柏拉图表示假说启发，我们使用从经典物理感知神经网络（PINN）替代模型提取的潜在表示，初始化量子模型的潜在变量，以求解伯格斯方程。随后在更高分辨率的解域上对初始化后的IQP模型进行适配。我们发现这种结构化初始化始终优于随机潜在初始化，在多种粘度设置下，可产生更优的适配行为与更强的生成准确率。这些结果表明，经典替代模型表示可为量子生成式模型提供有用的归纳偏置，并为基于IQP的学习提供了改进初始化的实用途径。
英文摘要
Quantum generative learning based on instantaneous quantum polynomial-time (IQP) circuits can benefit from efficient classical training strategies. A recent latent adaptation framework for IQP-based generative modeling transfers shared circuit parameters across instances of the same task with different hyperparameters while adapting only a low-dimensional latent variable for each new instance. However, existing approaches initialize this latent variable randomly, which can limit optimization efficiency and performance. In this work, we introduce a physics-informed latent initialization scheme for IQP generative learning to improve upon existing random initialization schemes. Motivated by the platonic representation hypothesis, we use latent representations extracted from a classical physics-informed neural network (PINN) surrogate to initialize the latent variables of the quantum model for the solution of the Burgers' equation. The initialized IQP model is then adapted on a higher-resolution solution domain. We find that this structured initialization consistently outperforms random latent initialization, yielding improved adaptation behavior and stronger generative accuracy across multiple viscosity settings. These results show that classical surrogate representations can provide useful inductive bias for quantum generative models and offer a practical route to improved initialization in IQP-based learning.
