In this paper, we propose a blueprint of a new deep network unfolding a baseline quantum mechanics-based adaptive denoising scheme (De-QuIP). Relying on the theory of quantum many-body physics, the De-QuIP architecture incorporates local patch similarity measures through a term akin to interaction in quantum physics. Our proposed deep network embeds both quantum interactions and other quantum concepts, mainly the Hamiltonian operator. The integration of these quantum tools brings a nonlocal structure to the proposed deep network that harnesses the power of the convolutional layers to enhance the adaptability of the model. Thus, recasting De-QuIP in the framework of a deep learning network while preserving the essence of the baseline structure is the main contribution of this work. Experiments conducted on the Gaussian denoising problem, chosen here for illustration purpose, over a large sample set demonstrate start-of-the-art performance of the proposed deep network, dubbed as Deep-De-QuIP hereafter. Based on the properties of De-QuIP, its intrinsic adaptive structure, Deep-De-QuIP network could be easily extended to other noise models.