Quantum circuit compilation is a challenging task required for the efficient implementation of quantum circuits in a quantum computer. Today, even if the algorithm for a given problem can be conceptually derived, the delicate nature of quantum systems, the high cost, and the limited availability of quantum computing infrastructure create the need for complex gate optimization routines. In this work, we propose to address this problem from a radically novel perspective of using human natural language, for example, English. Specifically, our method uses tools provided by the Quantum Natural Language Processing (QNLP) framework for reducing quantum circuit complexity. This recently invented discipline has identified strong links between human natural language and quantum computing. Given the fact that using QNLP, noisy intermediate scale quantum computing (NISQ) devices are now successfully being used to solve problems in natural language processing (NLP) and Artificial Intelligence (AI), in this work, we propose to ask the converse question: Can QNLP techniques be used to reduce the circuit-size and depth of quantum circuits, in turn contributing to quantum circuit optimization and compilation? We show that this is do-able and present a minimal theoretical proof of concept herewith. Note that this paper is not meant to be a comprehensive experiment results-based work, but instead is meant to be a theoretical vanguard one aimed to introduce the world to the usefulness of QNLP and subsequently initiate research and discussions in this direction.