Quantum Vision Theory in Deep Learning for Object Recognition - Cem Direkoglu; Melike Sah (IEEE Access, 2025)
We introduce a new perspective and a theory, called Quantum Vision (QV) theory in deep learning, for object recognition. The proposed theory is based on particle-wave duality of quantum physics. In quantum-scale, an object appears as a wave until it is observed, but after observation the object collapses into a solid object, called particle. In quantum world, every object has a ‘wave function’ that contains all the information about the object permitted by the uncertainty principle. Quantum-scale world looks different from our human-scale world. Attempts to relate the microscopic quantum world to our macroscopic world led to philosophical issues and questions. But what if the objects in human-scale world such as cats, dogs and bicycles have wave functions as well? And what will happen if we feed waves of objects to Deep Neural Networks (DNN) instead of collapsed still images of objects captured by cameras? This is the main contribution of our work. Inspired from quantum physics, we introduce a new perspective and theory, called Quantum Vision (QV) theory in deep learning that is a completely new perspective for object recognition. The proposed QV theory takes captured still images of objects, and converts them to information wave functions using a deep learning block that is called QV block. The proposed QV block is integrated into sequential CNNs, vision transformers and convolutional vision transformer to generate QV model variants for object classification. Extensive experiments are carried out on several datasets, and results demonstrate that QV model variants perform consistently better than standalone versions.