arXiv subjects

Hoa T. Nguyen

Publications and source records attributed to Hoa T. Nguyen.

10 recordsLinked to original sources

arXiv2026

QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization

Quantum cloud platforms need to dynamically orchestrate workloads across heterogeneous quantum computation backends whose noise profiles, qubit topologies, and queues vary over time. Existing orchestrators use noise-agnostic heuristics that ignore backend-specific errors, causing reduced execution fidelity, load imbalance, and frequent rescheduling. To address these challenges, we propose QCOEM - a Quantum Cloud Orchestration framework that leverages Evolutionary algorithms for Multi-objective optimization of quantum task scheduling. We compare NSGA-II and NSGA-III for jointly minimizing mean completion time, execution error rate, and load imbalance. To select schedules from a non-convex Pareto front, we apply an Augmented Achievement Scalarization Function (AASF) as a preference-based decision rule that maps the Pareto set to a single dispatchable schedule aligned with user priorities. Our extensive performance evaluation in a heterogeneous quantum cloud environment shows zero task rescheduling and about 30% higher mean fidelity than noise-agnostic heuristics, while maintaining bounded scheduling overhead. The experiment results indicate that our QCOEM framework can deliver stable, high-fidelity execution and lightweight resource management for quantum cloud computing.

cs.DC↗

arXiv2026

QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging

Quantum software bugs often yield silent, incorrect outputs rather than explicit errors, making them particularly difficult to detect and repair with conventional techniques. Although large language models (LLMs) have shown strong performance on classical software engineering tasks, their ability to debug quantum code remains largely unexplored. To bridge this gap, we propose QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs. We further conduct a comprehensive case study using QBugLM to benchmark two LLMs, Claude 4.6 Sonnet and Qwen3 Coder Next, across different prompting strategies, bug categories, and quantum programs. Our results show that iterative feedback is critical, as a single retry raises Pass@1 from below 25% to above 80%. Moreover, simpler structured prompting can even outperform Chain-of-Thought and ReAct for reasoning-capable models under fixed-resource constraints. Our work takes initial steps toward benchmarking LLM capabilities for debugging quantum programs and offers practical insights to support future efforts in automated quantum software repair.

cs.SE↗

arXiv2025

QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

Quantum cloud computing enables remote access to quantum processors, yet the heterogeneity and noise of available quantum hardware create significant challenges for efficient resource orchestration. These issues complicate the optimization of quantum task allocation and scheduling, as existing heuristic methods fall short in adapting to dynamic conditions or effectively balancing execution fidelity and time. Here, we propose QFOR, a Quantum Fidelity-aware Orchestration of tasks across heterogeneous quantum nodes in cloud-based environments using Deep Reinforcement learning. We model the quantum task orchestration as a Markov Decision Process and employ the Proximal Policy Optimization algorithm to learn adaptive scheduling policies, using IBM quantum processor calibration data for noise-aware performance estimation. Our configurable framework balances overall quantum task execution fidelity and time, enabling adaptation to different operational priorities. Extensive evaluation demonstrates that QFOR is adaptive and achieves significant performance with 29.5-84% improvements in relative fidelity performance over heuristic baselines. Furthermore, it maintains comparable quantum execution times, contributing to cost-efficient use of quantum computation resources.

quant-ph↗

arXiv2025

On the use of an advanced Kirchhoff rod model to study mooring lines

In this work, we investigate the application of an advanced nonlinear torsion- and shear-free Kirchhoff rod model, enhanced with a penalty-based barrier function (to simulate the seabed contact), intended for studying the static and dynamic behavior of mooring lines. The formulation incorporates conservative and non-conservative external loads, including those coming from the surrounding flow (added mass, tangential drag, and normal drag). To illustrate the favorable features of this model, we consider some key scenarios such as static configurations, pulsating force applications at the fairlead, and fluid-structure interaction between mooring lines and the surrounding flow. Verification against well-established solutions, including catenary configurations and OpenFAST simulations, shows excellent accuracy in predicting mooring line responses for a floating offshore wind turbine. Among the most important results, we can mention that under normal pulsating loads at the fairlead, the mooring line exhibits a transition from a drag-dominated regime at low frequencies to an added-mass-dominated regime at higher frequencies. Furthermore, tangential forcing at the fairlead reveals a strong coupling between axial and bending dynamics, contrasting with normal forcing scenarios where axial dynamics remain largely unaffected. These findings underscore the potential of the proposed approach for advanced mooring line simulations.

physics.flu-dyn↗

arXiv2024

DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)-based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81% to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.

cs.DC↗

arXiv2024

Quantum Serverless Paradigm and Application Development using the QFaaS Framework

Quantum computing has the potential to solve complex problems beyond the capabilities of classical computers. However, its practical use is currently limited due to early-stage quantum software engineering and the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. To address this issue, this chapter introduces the concept of serverless quantum computing with examples using QFaaS, a practical Quantum Function-as-a-Service framework. This framework utilizes the serverless computing model to simplify quantum application development and deployment by abstracting the complexities of quantum hardware and enhancing application portability across different quantum software development kits and quantum backends. The chapter provides comprehensive documentation and guidelines for deploying and using QFaaS, detailing the setup, component deployment, and examples of service-oriented quantum applications. This framework offers a promising approach to overcoming current limitations and advancing the practical software engineering of quantum computing.

cs.ET↗

arXiv2024

QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management

Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in cloud environments. Underpinned by extensibility, compatibility, and reusability principles, QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine for modeling dynamics of quantum cloud resources and task operations. We integrate the Gymnasium environment into our framework to support the creation of simulated environments for developing and evaluating reinforcement learning-based techniques for optimizing quantum cloud resource management. The QSimPy framework encapsulates the operational intricacies of quantum cloud environments, supporting research in dynamic task allocation and optimization through DRL approaches. We also demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems, demonstrating its potential as a useful framework for future quantum cloud research.

cs.ET↗

arXiv2024

Quantum Cloud Computing: A Review, Open Problems, and Future Directions

Quantum cloud computing is an emerging paradigm of computing that empowers quantum applications and their deployment on quantum computing resources without the need for a specialized environment to host and operate physical quantum computers. This paper reviews recent advances, identifies open problems, and proposes future directions in quantum cloud computing. It discusses the state-of-the-art quantum cloud advances, including the various cloud-based models, platforms, and recently developed technologies and software use cases. Furthermore, it discusses different aspects of the quantum cloud, including resource management, quantum serverless, security, and privacy problems. Finally, the paper examines open problems and proposes the future directions of quantum cloud computing, including potential opportunities and ongoing research in this emerging field.

cs.ET↗

arXiv2023

iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments

Today's quantum computers are primarily accessible through the cloud and potentially shifting to the edge network in the future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model hybrid quantum-classical computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.

quant-ph↗

arXiv2022

QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing

Recent breakthroughs in quantum hardware are creating opportunities for its use in many applications. However, quantum software engineering is still in its infancy with many challenges, especially dealing with the diversity of quantum programming languages and hardware platforms. To alleviate these challenges, we propose QFaaS, a novel Quantum Function-as-a-Service framework, which leverages the advantages of the serverless model and the state-of-the-art software engineering approaches to advance practical quantum computing. Our framework provides essential components of a quantum serverless platform to simplify the software development and adapt to the quantum cloud computing paradigm, such as combining hybrid quantum-classical computation, containerizing functions, and integrating DevOps features. We design QFaaS as a unified quantum computing framework by supporting well-known quantum languages and software development kits (Qiskit, Q#, Cirq, and Braket), executing the quantum tasks on multiple simulators and quantum cloud providers (IBM Quantum and Amazon Braket). This paper proposes architectural design, principal components, the life cycle of hybrid quantum-classical function, operation workflow, and implementation of QFaaS. We present two practical use cases and perform the evaluations on quantum computers and simulators to demonstrate our framework's ability to ease the burden on traditional engineers to expedite the ongoing quantum software transition.

quant-ph↗