Quantum Neural Networks in the NISQ Era: Architectures, Challenges, and Applications

Quantum Neural Networks (QNNs) have emerged as a promising paradigm at the intersection of quantum computing and machine learning. As we advance through the Noisy Intermediate-Scale Quantum (NISQ) era, these models offer both opportunities and challenges. On the one hand, QNNs promise novel ways of representing data and solving problems in domains ranging from pattern recognition to scientific simulation. On the other, their development is hindered by practical constraints such as hardware noise, limited qubit counts, and difficulties in training. This paper provides a concise overview of QNN architectures and training strategies, highlights key open challenges, and discusses their implications for near-term quantum computing. To make the field more accessible, we also provide a tutorial-style example that constructs and trains a simple four-qubit QNN classifier on the two-moons dataset. In this illustrative example, the QNN model achieves a test accuracy in the range of \(92 \pm 2\%\) , and we include a small multilayer perceptron as a classical baseline to contextualize performance. Our goal is to present a balanced resource that both introduces the fundamentals and reflects the current perspectives serving as a guide for newcomers and practitioners.