This 2026 arXiv preprint survey by Yifan Sun offers a systematic literature review of quantum neural network variants, including fully connected QNNs, quantum convolutional networks, equivariant architectures, Hopfield networks, Boltzmann machines, reservoir computing, and hybrid setups for reinforcement, generative, and transfer learning. As a preprint it lacks peer review and reports no new empirical benchmarks, instead summarizing accuracy, training time, and qubit-resource trade-offs drawn from prior theoretical and small-scale simulations. The work correctly flags that each QNN type trades expressivity for trainability, yet it underplays near-term hardware constraints such as decoherence and limited qubit connectivity that have repeatedly stalled claimed speed-ups. Cross-referencing with Biamonte et al.'s 2017 Nature review on quantum machine learning reveals a persistent pattern: theoretical kernels often assume fault-tolerant hardware still years away. Adding Preskill's 2018 NISQ framework shows why many surveyed QNNs remain proof-of-concept; the convergence narrative therefore hinges less on algorithmic novelty and more on whether error-corrected logical qubits arrive before classical transformers saturate scaling laws. Mainstream coverage misses this timeline mismatch, focusing on hype rather than the hardware-software co-design bottleneck the survey only implicitly acknowledges.