Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook

Abstract

We propose a quantum sidecar architecture family for future hybrid AI training and inference. The central idea is not to store an entire Transformer in a small quantum memory, nor to claim one-shot collapse into a fully trained model or an optimal answer. Instead, we identify two physically distinct operating modes for quantum co-processors attached to classical large-model pipelines. The first is a stateful protected-register mode, in which a protected register stores a reusable quantum resource while an ancilla or temporary register performs QND-style readout. The second is a stateless reset-and-reprepare mode, in which each query prepares a task-conditioned quantum circuit, evolves over bounded training or inference control variables, measures candidate signals, resets the qubits, and repeats. We simulate the stateful mode using 2/4/6/8 protected-qubit density-matrix QND-style parity readout with one ancilla and a Qiskit cross-check. For the stateless mode, we include both an abstract candidate-update sampler and a circuit-level QAOA-style statevector sampler over structured candidate landscapes, followed by reset-overhead sensitivity analysis. The resulting framework positions quantum sidecars as bounded signal generators for optimizer-side sampling, adapter or expert selection, retrieval, routing, and reasoning-path proposal. As a speculative outlook, we introduce quantum weight-state sidecars: restricted quantum representations over model-control variables, not direct encodings of complete classical weight tensors.