Using expert concept maps to identify mental models and intuitions related to quantum sensing

Quantum sensors, along with quantum computers and networks, are technologies that may offer advantages because of the unique behaviors of quantum objects. Quantum sensors are quantum objects (e.g., atoms, superconducting circuits, and photons) that exploit quantum phenomena, such as superposition, interference, and entanglement, to develop new sensor platforms with improved performance. Although quantum computing education is growing quickly, there has been less effort toward developing curricula for quantum sensing. Quantum sensors are diverse in their function and application, which may contribute to the lack of a coherent curriculum. In order to identify core ideas and their organization, we are conducting expert interviews with researchers in quantum sensing. A key feature of each interview is the creation of a detailed concept map about how a particular sensor works, which includes links to more fundamental concepts and the identification of specific ideas for teaching. Our analysis includes a high-level integration across multiple expert maps and a search for smaller networks of concepts that form mental models and intuitions that experts use to understand, design, experiment with, and improve quantum sensors. We will present examples of these mental models and how they can guide learning progressions from typical content in quantum-related courses (e.g., modern physics, quantum mechanics, or quantum information science) toward concepts and applications of quantum sensing.