Ultra-efficient physical field computing by complex-valued network quantization

Neural network quantization is an established technique for compressing real-valued models, but its application to complex-valued networks—essential in electromagnetics, acoustics, and quantum physics—remains underdeveloped. Conventional quantization methods treat real and imaginary components as independent channels, thereby disrupting the algebraic structure of complex multiplication and distorting essential phase relationships. To address this problem, we propose a real-imaginary joint quantization method that minimizes error propagation in complex multiplication and maintains coherence in phase-sensitive tasks, thereby preserving amplitude-phase fidelity during complex-valued inference. Combined with physics-aware adaptive precision training, this approach demonstrates outstanding performance across hologram generation, audio classification, wireless signal classification, and synthetic aperture radar signal recognition tasks. Compared to the state-of-the-art hologram generation model HoloNet, our approach achieves a 3.9 dB improvement in peak signal-to-noise ratio while reducing computational load and memory consumption by 99.1% and 99.8%, respectively. This research establishes a pathway toward lightweight, high-fidelity complex-valued neural networks for scientific computing and coherent signal processing.