A nonlinear quantum neural network framework for entanglement engineering

Multipartite entanglement is a key resource for quantum technologies, yet its scalable generation in noisy quantum devices remains challenging. Here, we propose a low-depth quantum neural network architecture with linear scaling with a novel approach to introducing activation functions, used for the task of entanglement engineering. As a testbed to demonstrate the clear advantage unlocked by the introduction of nonlinear activations, we run a Monte Carlo sampling over 10⁵ circuit topologies for pure noiseless states. Supported by the clear edge offered by our approach, we focus our attention on the noisy scenario; we employ the experimentally-accessible Meyer-Wallach global entanglement as a scalable surrogate optimization cost and certify entanglement using bipartite negativity. For mixed states up to ten qubits, the optimized circuits generate substantial entanglement across both symmetric and asymmetric bipartitions. These results established an experimentally motivated and scalable framework for engineering multipartite entanglement on near-term quantum devices, highlighting the combined role of nonlinearity and circuit topology that can achieve up to 20 qubits for very low noisy levels.