We present a quantum-inspired training method called Entanglement-Guided Stochastic Regularization (EGSR), which introduces structured noise into feature maps during training. To evaluate its effectiveness, we compare EGSR against standard Dropout under various image corruption scenarios, using the MNIST dataset padded to \(32 \times 32\) and replicated across three channels to suit CNN architectures. Unlike Dropout, which randomly zeros out activations, EGSR partitions channels into groups and injects shared Gaussian noise within each group, mimicking entangled feature interactions. Our experiments show that EGSR consistently improves accuracy on corrupted inputs, particularly at moderate noise levels, while remaining efficient and simple to implement. We also explore how entanglement rate and chunk size affect performance, and find that EGSR performs competitively across multiple corruption types, especially Gaussian and salt and pepper noise.