Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
This addresses computational and stability issues in deep learning for streaming data, but it appears incremental as it builds on existing quantum-inspired and integral-based methods without broad SOTA claims.
The paper tackles the challenge of real-time continuous learning over streaming data by introducing Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which use Feynman's differentiation under the integral sign to enable smoother and more stable learning dynamics, though no concrete performance numbers are provided.
Real-time continuous learning over streaming data remains a central challenge in deep learning and AI systems. Traditional gradient-based models such as backpropagation through time (BPTT) face computational and stability limitations when dealing with temporally unbounded data. In this paper, we introduce a novel architecture, Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which leverages the Feynman technique of differentiation under the integral sign to formulate neural updates as integrals over historical data. This reformulation allows for smoother, more stable learning dynamics that are both physically interpretable and computationally tractable. Inspired by Feynman's path integral formalism and compatible with quantum gradient estimation frameworks, QIDINNs open a path toward hybrid classical-quantum neural computation. We demonstrate our model's effectiveness on synthetic and real-world streaming tasks, and we propose directions for quantum extensions and scalable implementations.