Quantum Dot Cellular Automata (QCA) is an emerging nanotechnology that offers a promising alternative to traditional transistor-based circuits by lever- aging quantum dot interactions for computation. While QCA provides signif- icant advantages in terms of speed, power consumption, and miniaturization, it is also susceptible to various parasitic disorders which can impact the accuracy and reliability of QCA-based systems. This paper surveys the types of parasitic disorders that can affect QCA platforms and explores their im- plications in the context of identifying individuals. We examine how these disorders manifest, their potential sources, and the mitigation strategies that can be employed. Additionally, we discuss the use of QCA in biometric identification systems, assessing the resilience of these systems to parasitic disorders and proposing guidelines for robust design.