提出量子协处理器架构，分状态保护与无状态重准备两种模式，提升混合AI训练推理效率。
Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook
- 采用状态保护与无状态重准备两种量子协同模式，分别实现资源复用与任务定制化计算。
- 模拟显示2/4/6/8个保护量子比特的读出精度稳定，重置开销影响可量化分析。
- 适合研究量子增强型AI系统、模型路由与推理路径生成的学者参考。
我们提出一类面向未来混合人工智能训练与推理的量子协处理器架构。核心思想并非将完整Transformer存入小规模量子内存，也非宣称单次坍缩至全训练模型或最优解。相反，我们识别出两类物理上不同的量子协处理器工作模式：第一种为状态保护寄存器模式，其中保护寄存器存储可复用的量子资源，辅助量子比特执行类似于量子非破坏性（QND）的读出；第二种为无状态重置-重准备模式，每次查询均基于任务条件准备量子电路，在有限控制变量下演化，测量候选信号后重置量子比特并重复。我们使用2/4/6/8个保护量子比特的密度矩阵进行QND式偶校验读出模拟，并通过Qiskit验证结果。在无状态模式中，包含抽象候选更新采样器和结构化候选空间上的QAOA风格态矢量采样器，以及重置开销敏感性分析。该框架将量子协处理器定位为优化器侧采样的有限信号生成器，适用于适配器选择、专家选取、检索、路由与推理路径提案。作为前瞻性展望，我们引入量子权重态协处理器：对模型控制变量的受限量子表示，而非直接编码完整经典权重张量。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。