摘要：机器学习、联邦学习、量子机器学习以及经典—量子混合系统中的自适应优化工作流，通常会将控制器逻辑直接嵌入特定任务的训练脚本中。这种耦合限制了跨执行环境的复用性、可复现性、安全验证和公平比较。本文提出 DeepUnfolding Agent，这是一个由 schema 驱动的 FastAPI 平台，将深度展开式自适应控制作为外部软件服务提供。外部执行器注册策略 schema，提交轮级诊断变量，接收受 schema 约束的控制动作，执行一轮优化，并返回用于控制器更新的反馈。该框架通过策略注册表、诊断到状态适配器、控制器工厂、安全防护机制、执行器抽象、反馈更新路径、SQLite 持久化层、Docker 部署和可复现基准脚本，将控制器智能与特定领域执行分离。该实现支持经典、量子感知、混合、Qiskit Aer 以及可选的 IBM 量子处理执行路径。我们通过单元、schema、API、安全、编排、量子执行、检查点和 Docker 测试验证该软件，并展示了金融、医疗保健、基因组学、交通、含噪量子模拟及 IBM Quantum 硬件支持执行中的外部用户固定式与自适应工作流。结果表明，DeepUnfolding-Agent 为异构优化工作流提供了可复用且可追踪的自适应控制层。我们将量子实验解读为 API 级量子控制和硬件可移植性的证据。

Abstract:Adaptive optimization workflows in machine learning, federated learning, quantum machine learning, and hybrid classical quantum systems often embed controller logic directly inside task specific training scripts. This coupling limits reuse, reproducibility, safety validation, and fair comparison across execution environments. This paper presents DeepUnfolding Agent, a schema-driven FastAPI platform that exposes deep unfolded adaptive control as an external software service. External executors register a strategy schema, submit round-level diagnostic variables, receive schema-bounded control actions, execute one optimization round, and return feedback for controller update. The framework separates controller intelligence from domain specific execution through a strategy registry, diagnostic to state adapter, controller factory, safety guard, executor abstraction, feedback update path, SQLite persistence layer, Docker deployment, and reproducible benchmark scripts. The implementation supports classical, quantum-aware, hybrid, Qiskit Aer, and optional IBM quantum processing execution pathways. We validate the software through unit, schema, API, safety, orchestration, Quantum execution, checkpointing, and Docker tests, and demonstrate external user fixed versus adaptive workflows across finance, healthcare, genomics, transport, noisy quantum simulation, and hardware backed IBM Quantum execution. The results show that DeepUnfolding-Agent provides a reusable and traceable adaptive control layer for heterogeneous optimization workflows. Our quantum experiments are interpreted as API level quantum control and hardware portability evidence.