DeepUnfolding Agent: A Classical Quantum Adaptive Control API

arXiv:2610.20615 · cs.CL, cs.LG · Submitted 2026-07-21 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: Today's paper: "DeepUnfolding Agent: A Classical Quantum Adaptive Control API".

Jane: The gist DeepUnfolding-Agent presents a schema-driven adaptive-control API that decouples deep-unfolded controller intelligence from external optimization workflows, enabling reusable control logic across classical, quantum, and hybrid systems.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at the paper "DeepUnfolding Agent: A Classical Quantum Adaptive Control API" and it’s about how to make these adaptive optimization workflows reusable. It tackles a problem where the controller logic is usually tied up inside task-specific training scripts, which limits you from reusing that logic elsewhere.

Jane: That sounds really practical, Tom. So instead of baking the controller right into the training pipeline for one specific experiment, they’re proposing a way to treat it like an external service that you can plug into different setups.

Lu: Exactly. The core idea is to decouple the deep-unfolded controller intelligence from whatever task you're running—whether that's classical, quantum, or some hybrid thing—using a schema-driven interface instead of embedding the logic directly.

Meng: When you talk about decoupling, what does that actually mean for someone like me who has to worry about deploying something at scale? Does this mean I have to rewrite my whole training pipeline just to swap out the controller?

Tom: Not at all, Meng. The paper presents DeepUnfolding-Agent as a reusable software service accessed through a FastAPI platform where external executors register a strategy schema and then submit round-level diagnostic variables.

Jane: Okay, so the external executor is responsible for the heavy lifting like data loading or model training, and it sends in diagnostics that tell the controller what's happening in that specific round.

Lu: Right. And this brings us to the formal control contract they define, where they have a schema S which includes an ordered diagnostic set DS, a bounded control domain BS with lower and upper admissible values for each control variable, and then the final control vector ut is what you get after clipping and validation according to those bounds.

Meng: So it’s like setting strict boundaries on what the controller is allowed to suggest before it even gets sent back into the system. That sounds like a good way to manage risk in production environments.

Tom: Precisely, Meng. And this structure allows for strategy agnosticism, meaning the core API supports classical FL, QFL-style workflows, hybrid optimization, and external domain executors without needing changes to the main framework.

Jane: That’s a big deal for researchers who work across different domains; they don't have to learn a completely new system just to switch between classical and quantum setups.

Lu: And they show how the deep-unfolding controller, which is represented as f(K)theta over R m to R q, takes the state vector st derived from those diagnostics and generates raw control suggestions that are then projected into that feasible set ut by applying ΠBS,TS which handles clipping and type conversion based on the schema.

Title and authors: Meng: So you get these raw suggestions, but they go through a safety guard first to make sure they’re actually valid before the external system uses them? That’s a step up from just letting an optimization script run wild.

Tom: Exactly, Meng. Then the executor performs one optimization round using those schema-bounded controls zt and returns feedback rt, which is then used by the controller to update its parameters theta t+one through a feedback mapping ψ <ref:2610.20615#pg1>.

Jane: It’s a closed loop where the system observes, suggests within safe limits, executes, gets feedback on how well it did in that round, and then learns from that experience to make better suggestions next time.

Lu: The paper details the control flow where diagnostics are mapped into an ordered state vector st = ϕS(dt), and this state vector feeds into the controller f(K)theta to generate those raw controls.

Meng: But how does it handle the different types of systems? If I’m running a classical simulation, is it treated the same way as if I were using a quantum execution wrapper?

Tom: It uses distinct wrappers for classical and quantum execution pathways, including Qiskit Aer as a default reproducible quantum backend that doesn't need cloud credentials.

Jane: That’s good for reproducibility because you can test the whole loop locally without needing access to specific hardware queues or cloud resources.

Lu: They validate this whole system through layered testing, including unit, schema, API, and safety checks to ensure the diagnostic adapter mapping is correct and the safety-bounded projection layer correctly implements equation six to guarantee only valid values are returned.

Meng: What about running this thing on real traffic? I mean, what happens when you try to use this adaptive control layer on actual production data where things aren't perfectly clean?

Tom: Meng, the paper shows external benchmarks across financial fraud detection, medical imaging, genomic bioinformatics, clinical diagnosis and vehicle trajectory analysis. They claim that in a classical workflow, it increases the final Macro-F1 score from sixty-two point six percent to seventy-two point nine percent, and reduces validation loss from zero point six one three down to zero point three zero four compared to a fixed baseline for the same task >

Jane: That's a significant jump in performance, especially with the speed improvement they also noted, achieving something about five times faster than the fixed baseline in that classical scenario.

Lu: Across all those different workflows tested, the paper states that DeepUnfolding-Agent improves final loss by thirty-seven point three percent and reaches the target something like three point four four times faster than the fixed baseline.

Title and authors: Meng: Those are strong numbers, but I always wonder about the real-world cost of setting up this whole infrastructure—the FastAPI service, the SQLite persistence layer, all that overhead when you’re trying to deploy a model that needs to run on a tight budget.

Tom: The paper focuses heavily on the architecture and reusability as a software artifact, showing how it separates the intelligence from the execution, which is what makes it adaptable for various tasks.

Jane: It moves adaptive control from being tightly coupled training code to something that can be swapped in based on whatever experiment you're running next.

Lu: The limitation they mention is that while they provide a reusable framework, the specific controller update rule A denotes the feedback-based controller update rule, and it’s designed around observed state, bounded controls, and feedback rt.

Meng: So if I want to deploy this for something completely new that doesn't fit their defined diagnostic set DS, I can't just plug it in without rewriting part of the adapter layer?

Tom: The architecture is designed to be flexible with the strategy registry, which handles registering new strategies and adapting the schema based on what diagnostics are actually being reported by the external executor.

Jane: So it’s not a rigid system where you have to define everything upfront for every single use case? It seems designed to grow with different types of problems.

Lu: The paper does state that they provide a complete step-by-step reproducibility checklist in the supplementary section S8, which is important because the whole point is traceability across all these layers.

Meng: Traceability is important, but what about ensuring the feedback mapping ψ accurately captures all the signal from a complex quantum execution environment? Is that always straightforward?

Tom: The paper uses a diagnostic adapter to handle this mapping defined in equation three and it’s designed to be robust enough to translate those varied diagnostics into the state vector st <ref:2610.20615#pg1>.

Jane: It seems like they've built this thing specifically to handle the complexity of bridging different execution environments with a consistent interface for control.

Lu: So, in summary, the DeepUnfolding Agent is presented as a schema-driven API that allows you to use deep-unfolded adaptive control logic across classical, quantum, and hybrid systems by separating the controller intelligence from domain-specific execution through a formal contract.

Meng: It’s about providing that reusability so researchers don't have to start from scratch every time they want to test a new optimization strategy on their data.

Title and authors: Tom: Right. It provides a traceable layer for adaptive control that lets you compare different approaches fairly because the execution environment is standardized via the schema.

Jane: And it shows concrete improvements in performance metrics across several real-world applications, moving those numbers from sixty-two point six to over seventy-two point nine in one case.

Lu: It sets up a solid foundation for future work, like extending it with larger multiseed evaluations and even more expressive quantum models, although the paper notes that the specific controller update rule A is tailored to their current setup.

Meng: I just hope that the deployment aspect matures fast enough so this isn't just something sitting in a research repository but something actually running reliably on a busy server.

Tom: It’s definitely aimed at being an open-source research software artifact, providing the necessary tools for experimentation and then moving toward production features like secure authentication later on.

Jane: So we have this DeepUnfolding Agent, a schema-driven API that handles the entire lifecycle of adaptive control across different system types, from defining the strategy to getting feedback for controller updates.

Lu: That’s essentially turning adaptive optimization into a problem that can be solved by an external, reusable service interface rather than embedding it in every single experiment script.

Meng: It’s about making the process of adapting control logic much more portable and less tied to any one specific piece of code.

Tom: So we're looking at DeepUnfolding Agent as a way to gain reusability for adaptive agent services, supporting both current classical learning systems and those emerging quantum-aware machine learning pipelines.

Jane: It’s an API that lets you define the contract—the diagnostics, the controls—and then any executor can plug into it and get adaptive behavior back in exchange for feedback on how well it performed.

Lu: We saw how the paper uses this to provide a reusable and traceable adaptive-control layer for heterogeneous optimization workflows, which is what makes it valuable across different domains.

Meng: It’s about giving you that unified diagnostic–control–feedback API with bounded controls so you can manage the complexity without getting overwhelmed by the specifics of each backend.

Tom: So to wrap up on this DeepUnfolding Agent paper, it establishes a reusable software foundation for adaptive-agent services that can support both classical learning systems and emerging quantum-aware machine learning pipelines.

Jane: It’s a framework that lets you define the contract—the diagnostics, the controls—and then any executor can plug into it and get adaptive behavior back in exchange for feedback on how well it performed.

The paper's summary: Tom: So we're looking at the summary of DeepUnfolding Agent now and what it actually means for us on the ground, Jane?

Jane: The paper basically boils down to this: they’ve built a software service, an API, that lets you take deep-unfolded controller intelligence and use it across different types of systems—classical, quantum, hybrid—without having to rewrite the core logic every time.

Tom: Right. It’s about separating the thinking part from the execution part so you can reuse that intelligence everywhere.

Jane: Exactly, and they do this using a schema-driven interface. You define a contract for diagnostics and controls, like what information you report back in each round of optimization, and the agent enforces those rules automatically.

Tom: I see how that works mechanically. The controller takes that diagnostic info, turns it into a state vector, generates raw suggestions based on its learned parameters theta, and then—crucially—it projects those suggestions through safety checks to make sure they fit the bounds you set.

Jane: That bounding mechanism is really important for me to understand. It means the system doesn't just suggest whatever looks good; it’s actively constrained by the rules you defined in that schema, like maximum values or minimum constraints on your control variables.

Tom: Yeah, and then when the external executor runs an optimization round with those bounded controls, it sends back feedback, and the agent uses that feedback to update its own parameters theta for the next round of suggestions. It’s a closed loop of observation and learning.

Jane: It moves adaptive control away from being something baked deep inside a single training script for one specific task, making it a reusable component you can plug into different experimental pipelines. That reusability is what they really want to highlight here.

Tom: And the results they show are pretty solid, especially when comparing it against fixed baselines in things like financial fraud detection and medical imaging. They're talking about significant jumps in performance metrics, not just small tweaks.

Jane: It’s powerful because it shows a unified way to approach optimization across those diverse fields, whether you’re dealing with classical simulation or something quantum-aware. But what they also flag is that the controller update rule itself is specific to how they set up the feedback mapping.

Tom: That means if you want to use this for something completely different, you might need to tweak that update rule part, even though the rest of the API structure stays consistent. It’s flexible but not completely plug-and-play for every single scenario.

Jane: So it’s a very robust foundation for building adaptive agent services, but it still requires you to define that initial contract carefully so you can leverage the reusability they offer. Where do we go from here?

Tom: Well, the paper points toward future work like using bigger multiseed evaluations and maybe even more complex quantum models. We’ll talk about how those next steps might challenge this architecture in our next segment.

The paper's improvements: Tom: So we’re looking at what they actually suggest to improve DeepUnfolding Agent now, Jane?

Jane: They focus on three main areas: making it more flexible, making the control feedback smarter, and finally, expanding what it can handle.

Tom: Flexibility means they want to make sure that strategy registration is truly robust so you can easily plug in new control logics without breaking the whole API.

Jane: And that ties into their idea for a more expressive controller update rule, which they call A. They suggest making that rule more dependent on the observed state and those bounded controls, along with penalties for stability and resource usage.

Tom: So they're pushing the controller to be less reactive to just the immediate feedback and more aware of the bigger picture—like how stable or resource-heavy a certain control action is.

Jane: Exactly, because they want that adaptation loop to be more grounded in real operational constraints, not just abstract mathematical signals.

Tom: And on the hardware side, they want to extend this to include more expressive quantum models and broader hardware execution pathways beyond just Qiskit Aer.

Jane: That’s significant because it shows the framework is built for future growth, not just for what it can do right now. It’s designed to be extensible across different computing paradigms.

Tom: Meng, from an engineering standpoint, they are trying to solve the problem of deployment—making sure this complex logic doesn't just sit in a research repo but actually runs reliably when you need it in production.

Jane: Right, so the implication is that this isn't just a neat proof-of-concept; it’s aiming to be a foundation for deployable adaptive agents in both classical and quantum environments.

Tom: The paper mentions they plan to do larger multiseed evaluations next, which means they’ll test how well this works when you run the whole thing many times with slightly different starting conditions.

Jane: That’s important because it tests the robustness of the learning process itself, not just a single lucky run. It moves them toward more reliable performance guarantees for the adaptive control part.

Tom: So they’re really pushing for this to be a production-ready software artifact that handles the complexity of heterogeneous optimization workflows with traceable results.

Jane: And that leads us to thinking about how this framework fits in with other papers we’ve been hearing about, like those focusing on memory construction or uncertainty quantification.

Tom: Yeah, the way they handle that diagnostic mapping—how they translate raw system signals into a usable state vector—is actually really interesting when you compare it to how other agents manage context.

Jane: It’s a bridge between the high-level optimization goals and the low-level mechanics of what's happening in the external system, which is where we might see some cool connections to those memory papers.

Conclusion: Tom: So to wrap up on DeepUnfolding Agent: A Classical Quantum Adaptive Control API, we’re looking at how this whole system fits together and what it means for using AI in real research workflows.

Jane: Basically, they’ve established a reusable software foundation that lets you use deep-unfolded adaptive control logic across classical, quantum, and hybrid systems by separating the controller intelligence from the execution logic through a formal contract.

Tom: It boils down to giving researchers a traceable layer for adaptive control that lets them compare different approaches fairly because the execution environment is standardized via that schema.

Jane: And they’ve shown it works in practice, with concrete performance gains across things like financial fraud detection and medical imaging, moving metrics from sixty-two point six to over seventy-two point nine in one specific case.

Tom: Those performance numbers are solid because they come from testing against a fixed baseline, which is the kind of validation that matters when you’re shipping something.

Jane: It changes things for someone just listening to the show by showing that adaptive control isn't just theoretical; it can actually translate into measurable improvements in complex, real-world optimization problems.

Lu: From my side at Tsinghua, I think the architecture is really smart because it’s designed to be strategy agnostic, meaning you can swap out the specific control logic easily while keeping your diagnostic contract consistent.

Meng: But for deployment, we need to remember that this framework has specific update rules tied to its feedback mapping; if you want to deploy it on a completely new system that doesn't fit their defined diagnostics, you'll still have work to do on the adapter layer.

Lalam: I can confirm the literature shows this is a very solid starting point because it addresses the core problem of coupling controller logic directly into task-specific scripts.

Tom: So, this paper sets up a reusable software foundation for adaptive agent services that supports both current classical learning systems and emerging quantum-aware machine learning pipelines.

Lu: The next step, as they suggest, involves larger multiseed evaluations to really stress-test how stable that learning process is across different starting points.

Meng: I just hope that the deployment aspect matures fast enough so this isn't just something sitting in a research repository but something actually running reliably on a busy server.

Lalam: I’m confident this work provides the necessary structure for building more powerful adaptive control agents in any domain we look at next.

School of Information Technology, Deakin University

cs.CL, cs.LG

Submitted: 2026-07-21

Updated: 2026-07-21

Code: https://github.com/shanikairoshi/DeepUnfolding-Agent

Project page: https://deepunfolding-agent.github.io

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 83/100

The gist: The gist DeepUnfolding-Agent presents a schema-driven adaptive-control API that decouples deep-unfolded controller intelligence from external optimization workflows, enabling reusable control logic

Key concepts

Deep Unfolding: This is a learnable mechanism that adapts control variables across multiple iterative optimization rounds. It involves an unfolding process where the controller's parameters are trained to generate control suggestions based on the current state of the optimization.
Strategy Schema: A formal contract defining how an external executor interacts with the agent. It specifies which diagnostic variables to report (DS), what constraints apply to each variable (US), and what the safe bounds for control actions are (BS). This ensures consistent communication between different execution environments.
Bounded Control Domain: This defines the set of permissible values for every control variable, ensuring safety and feasibility during optimization. The system clips, converts, and validates raw suggestions to ensure that only controls within these defined lower ($\ell_j$) and upper ($u_j$) bounds are returned to the executor.
External Executor Protocol: A standardized interface allowing external software (classical or quantum) to interact with the DeepUnfolding-Agent API. The executor submits diagnostic data, receives a schema-bounded control action, executes an optimization round, and returns feedback for controller updates.

Terminology

Abstraction — 1× in this paper · explained in 1 episode
Benchmark — 1× in this paper · explained in 2 episodes
Federated Learning — 1× in this paper · explained in 17 episodes

Summary

The gist DeepUnfolding-Agent presents a schema-driven adaptive-control API that decouples deep-unfolded controller intelligence from external optimization workflows, enabling reusable control logic across classical, quantum, and hybrid systems.

Core Concept and Problem Addressed

Iterative optimization often embeds controller logic directly inside task-specific training scripts, which limits reuse and reproducibility Existing adaptive controllers are commonly implemented as taskspecific scripts coupled to a particular dataset, model, optimizer, simulator, or experimental pipeline This coupling reduces reproducibility and limits the practical value of adaptive optimization methods as reusable software components Deep unfolding provides a learnable mechanism for adapting control variables across iterative optimization rounds A reusable software architecture is therefore needed to separate controller intelligence from task execution through a strategy-agnostic, schema-driven interface.

API Design and Strategy Agnosticism

DeepUnfolding-Agent exposes deep-unfolded adaptive control through a modular FastAPI service with reusable components for strategy registration, schema adaptation, controller creation, bounded control suggestion, execution routing, feedback collection, controller update, and experiment logging. The primary contribution is an API-level abstraction that separates deep-unfolded controller intelligence from the external execution logic in classical, quantum, and hybrid workflows. The framework supports classical, quantumaware, hybrid, Qiskit Aer, and optional IBM quantum processing execution pathways.

Formal Control Contract

The formal control contract defines the interaction between the external executor and the API through a schema. For each round t = 1,..., T, an external executor Et manages the workflow. The executor reports a named diagnostic vector dt = h(t), d(t),..., d(m)t i, Diagnostics may include validation loss, gradient norm, update norm, data heterogeneity, shot noise, parameter-shift variance, circuit depth, resource usage, or round index. A strategy schema is denoted by S = (DS, US, BS,TS), where DS is the ordered diagnostic set. The bounded control domain is BS = Y q j=1 [l j, uj], where l j and uj are the lower and upper admissible values of the j-th control variable. The final control vector returned by the API is ut = ΠBS,TS (ˆut), where ΠBS,TS denotes clipping, type conversion, and safety validation according to the schema bounds, data-type constraints.

Control Flow and Adaptation Loop

The overall control objective is to select schema-valid controls that improve the external workflow objective while satisfying all bounds. The process follows a loop where diagnostics are mapped into an ordered state vector st = ϕS(dt). The deep-unfolding controller with K unfolding steps is represented as f(K)θ: R m → R q, where θ denotes the trainable controller parameters. Given the state vector st, the controller generates raw control suggestions ˆut = f(K)θ(st). These raw controls are first projected into the schema-defined feasible set: ut = ΠBS,TS (ˆut), where ΠBS,TS denotes clipping, type conversion, and safety validation according to the schema bounds, data-type constraints. The executor then performs one optimization round using the bounded controls: zt = Et(ut). A feedback mapping extracts the controller-update signal from these outputs: rt = ψ(zt). The controller is updated using the observed state, bounded controls, and feedback: θt+1 = A(θt, st, ut,rt), where A denotes the feedback-based controller update rule.

Validation and Execution Pathways

The software is validated through layered testing, including unit, schema, API, safety, orchestration, Quantum execution, checkpointing, and Docker tests. The diagnostic adapter implements the mapping defined in Equation 3. The safety-bounded projection layer implements Equation 6 to ensure only valid and safe values are returned to the executor. The external executor protocol allows classical and quantum workflows to interact with the API through a consistent diagnostic, control, and feedback interface. Qiskit Aer is used as the default reproducible quantum backend because it does not depend on cloud credentials or queue availability.

External Benchmarks and Results

The external-user benchmark uses datasets from financial fraud detection, medical imaging, genomic bioinformatics, clinical diagnosis, and vehicle trajectory analysis. The results show that DUAgent provides a reusable and traceable adaptive-control layer for heterogeneous optimization workflows. In the classical workflow, DUAgent increases final Macro-F1 from 62.6% to 72.9%, reduces final validation loss from 0.613 to 0.304, and achieves the target 5.00× faster than the fixed baseline. Across all workflows, DUAgent improves final loss by 37.3% and reaches the target 3.44× faster than the fixed baseline.

Conclusion and Future Work

DeepUnfolding-Agent establishes a reusable software foundation for adaptive-agent services that can support current classical learning systems and emerging quantum-aware machine-learning pipelines. Future work will extend DUAgent through larger multiseed evaluations, stronger domain-specific baselines, more expressive quantum models, broader hardware-backed execution, and production-oriented features such as secure authentication. The complete step-by-step reproducibility checklist is provided in Supplementary Section S8.

Software Availability

DeepUnfolding-Agent is released as an open-source research software artifact. The public project website provides documentation, overview material, and links to the implementation repository. The repository contains the FastAPI service, controller backends, strategy schemas, external-user runners, benchmark scripts, Docker configuration. Project website: https://deepunfolding-agent. Source code: https://github.com/shanikairoshi/.

References

[1] S. R. Pokhrel, J. Choi, Federated learning with blockchain for autonomous vehicles: Analysis and design challenges, 17

[2] S. R. Pokhrel, Deakin’s quantum machine learning opensource research, Australian Defence Science, Technology and Research (ADSTAR) Summit 2024

[3] H. T. Nguyen, M. Usman, R. Buyya, iquantum: A toolkit for modeling and simulation of quantum computing environments, Software: Practice and Experience 54 (6) (2024) 1141–1171

[4] P. Kairouz, H. B. McMahan, Advances and open problems in federated learning, Foundations and trends in machine learning 14 (1-2) (2021) 1–210

[5] M. Cerezo, A. Sone, T. Volkoff, L. Cincio, P. J. Coles, Cost function dependent barren plateaus in shallow parametrized quantum circuits, Nature communications 12 (1) (2021) 1791

[6] V. Monga, Y. Li, Y. C. Eldar, Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing, IEEE Signal Processing Magazine 38 (2)

Improvements for AI systems

Adaptive control layer for heterogeneous workflows: DUAgent provides a reusable and traceable adaptive-control layer for heterogeneous optimization workflows, enabling researchers to apply a unified diagnostic–control–feedback API with bounded controls across classical, quantum, hybrid, and external domain-specific execution environments.
Strategy-agnostic interface for controller reuse: The framework introduces a strategy-agnostic diagnostics–controls contract, allowing the core API to support classical FL, QFL-style workflows, hybrid optimization, and external domain executors without changes to the core API.
Bounded control enforcement for safety: The system implements a mechanism where suggested controls are projected into a feasible set via ut = ΠBS,TS (6), ensuring that the executor receives only bounded and valid controls to prevent unsafe applications in external workflows.
Closed-loop feedback adaptation: The controller iteratively updates its policy based on multi-objective signals defined by an objective function like Ft = wvalL val t + wstabomega stab t + wresomega res t + wlatomega lat t, allowing the system to adapt based on validation loss, stability penalty, resource penalty, and execution cost.
Quantum-aware control for QML: The API enables control over quantum variables such as theta learning rate, shot count, circuit depth, entanglement depth, and measurement reuse, supporting quantum-aware controls within the same unified interface as classical ones.

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.