Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent
Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou; 27(38):1−51, 2026.
Abstract
Complex-valued neural networks potentially possess better representations and performance than real-valued counterparts when dealing with some complicated tasks such as acoustic analysis, radar image classification, etc. Despite empirical successes, it remains unknown theoretically when and to what extent complex-valued neural networks outperform real-valued ones. We take one step in this direction by comparing the learnability of real-valued neurons and complex-valued neurons via gradient descent. We theoretically show that a complex-valued neuron can learn functions expressed by any one real-valued neuron and any one complex-valued neuron with convergence rates $O(t^{-3})$ and $O(t^{-1})$ where $t$ is the iteration index of gradient descent, respectively, whereas a two-layer real-valued neural network with finite width cannot learn a single non-degenerate complex-valued neuron. We prove that a complex-valued neuron learns a real-valued neuron with rate $\Omega (t^{-3})$, exponentially slower than the linear convergence rate of learning one real-valued neuron using a real-valued neuron. We then reparameterize the phase parameter of the complex-valued neuron and prove that a reparameterized complex-valued neuron can efficiently learn a real-valued neuron with a linear convergence rate. We further verify and extend these results via simulation experiments in more general settings.
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