Multi-head Quantum Neural Networks: Replacing Transformer Blocks with NISQ Modules inside a 30-Billion-Parameter LLM

arXiv:2611.15250v1 Announce Type: new Abstract: We introduce the multi-head quantum neural network (QNN), a quantum module compatible with noisy intermediate-scale quantum (NISQ) devices that can replace transformer blocks inside pre-trained large language models (LLMs) with little loss in accuracy and with far fewer parameters than the blocks it replaces. Encoding a model's $D$ hidden neurons into a qubit register is the obstacle: amplitude encoding scales poorly in circuit depth and angle encoding in qubit count, so neither is practical at production-scale width. We instead partition the hidden space into $k$ disjoint heads, angle-encode each into an $n_q$-qubit register, process the heads in parallel, and couple them through constrained classical linear layers, the mixers.