PCPL 让物理系统自己学会学习，做物理计算或类脑计算的团队值得关注——它可能开启无需数字芯片的自主训练新范式。
研究人员提出了一种名为Perturbative Contrastive Physical Learning (PCPL) 的通用框架，让物理系统能够通过对比不同条件下的响应来学习，无需外部处理器或显式反向传播。PCPL统一并扩展了平衡传播和频率传播等方法，通过测量物理状态之间的对比来驱动参数更新。该框架在弹簧网络和连续变量光子电路两种平台上成功演示了分类和模拟乘法任务。这一进展为构建更自主的物理学习系统迈出了重要一步。
Perturbative Contrastive Physical Learning
Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slightly different conditions provides a mechanism for learning. Here, we introduce Perturbative Contrastive Physical Learning (PCPL), a general framework in which learning emerges from measurable contrasts between physical states produced by controlled changes to inputs, boundary conditions, parameters, or interpreter functions. PCPL unifies and extends prior approaches: Equilibrium Propagation is rooted in contrasts between free and nudged equilibria in energy-based systems, while Frequency Propagation corresponds to contrasts extracted from sinusoidally driven, frequency-demodulated responses. We show that contrast-driven updates can reflect either local sensitivities or global inverse-problem structure, yet do not require centralized gradient computation. Instead, effective learning geometry emerges implicitly from the system's own physical response, allowing learning behavior to arise without an external processor or explicit backpropagation. We demonstrate PCPL in two platforms: (i) spring networks that update bond stiffness using measured displacements and forces, and (ii) continuous-variable photonic circuits trained via x quadrature measurements and finite-difference estimates of the Jacobian. Both platforms successfully learn classification tasks. We further show that a continuous-variable photonic circuit can be trained to implement analog multiplication, illustrating a step toward more autonomous physical learning systems.