Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware

We implement a quantum neural network on trapped-ion and IBM superconducting quantum computers for Modified National Institute of Standards image classification. Feedforward is realized through qubit rotations conditioned on measurement outcomes from previous layers. The network is trained classically, while inference is performed experimentally on quantum hardware. A tunable interpolation parameter connects the classical and quantum regimes. Moderate values improve classification performance by introducing measurement uncertainty. For borderline images misclassified classically but correctly identified quantum mechanically, we observe strong deviations from idealized simulations due to physical noise, consistent with fluctuations between nearby minima in the classification landscape. We further benchmark noise by inserting additional single- and two-qubit gate pairs into the circuits. Our results motivate more complex quantum neural networks on noisy intermediate-scale quantum devices and suggest a possible route toward classically nonsimulable architectures.