Abstract Quantum Cloud Computing (QCC) is a practice of setting up the cloud platform for delivering computing assets over the internet via a pay-as-you-go model with the help of Quantum Computing (QC) paradigm. Real-time applications have scrupulous compliance regarding performance requirements due to the low-speed of traditional computers. Estimating cloud data center asset usage is a challenging task due to its dynamic nature. It employs a contemporary model to precisely estimate data center CPU utilization and applies an effective resource controller for optimized resource allocation using quantum computers. The proposed design ensures efficient resource estimation, scaling up or down based on predictions. An efficient dynamic resource controller is crucial to solving the scaling process with quantum computing support. Existing systems use a Reinforcement-Based resource controller with a Markov decision process that decides based on the current state of the environment, leading to long scaling and processing times. Our proposed model, the Prediction-Based Offline Reinforcement Learning (PB-ORL) Model, enhances this by considering historical information for prediction-based decisions. This approach achieves accurate and high-performance prediction, optimizing resource allocation proactively and dynamically. The model is analyzed using a real cloud data set with quantum cloud and machine learning approaches, which reduces latency and bandwidth traffic. Empirical results show that the proposed quantum computer-based machine learning approach outperforms previous methods, achieving 30–50% improved accuracy in CPU resource utilization and reducing time complexity by 33–42% in resource allocation.
Abstract Quantum Cloud Computing (QCC) is a practice of setting up the cloud platform for delivering computing assets over the internet via a pay-as-you-go model with the help of Quantum Computing (QC) paradigm. Real-time applications have scrupulous compliance regarding performance requirements due to the low-speed of traditional computers. Estimating cloud data center asset usage is a challenging task due to its dynamic nature. It employs a contemporary model to precisely estimate data center CPU utilization and applies an effective resource controller for optimized resource allocation using quantum computers. The proposed design ensures efficient resource estimation, scaling up or down based on predictions. An efficient dynamic resource controller is crucial to solving the scaling process with quantum computing support. Existing systems use a Reinforcement-Based resource controller with a Markov decision process that decides based on the current state of the environment, leading to long scaling and processing times. Our proposed model, the Prediction-Based Offline Reinforcement Learning (PB-ORL) Model, enhances this by considering historical information for prediction-based decisions. This approach achieves accurate and high-performance prediction, optimizing resource allocation proactively and dynamically. The model is analyzed using a real cloud data set with quantum cloud and machine learning approaches, which reduces latency and bandwidth traffic. Empirical results show that the proposed quantum computer-based machine learning approach outperforms previous methods, achieving 30–50% improved accuracy in CPU resource utilization and reducing time complexity by 33–42% in resource allocation.
Mobile edge computing (MEC) has revolutionized the way computational tasks are offloaded and latency is reduced by leveraging edge servers close to end devices. Efficient resource allocation and task offloading are crucial for enhancing system performance in MEC environments. Traditional reinforcement learning (RL) approaches have shown promise in optimizing resource allocation and task offloading problems. However, they often face challenges such as high computational complexity and the need for extensive training data. Quantum reinforcement learning (QRL) emerges as a promising solution to overcome these limitations by leveraging quantum computing principles to enhance efficiency and scalability. In this paper, we propose a hybrid quantum-classical non-sequential model for joint resource allocation and task offloading in MEC systems. Our model combines the advantages of RL in handling environmental dynamics and quantum computing in reducing adjustable parameters and accelerating the training process. Extensive experiments demonstrate that our proposed algorithm can achieve higher training and inference performance under various parameter settings compared to traditional RL models and previous QRL models.
Quantum entanglement is a fundamental resource in quantum computing, enabling phenomena like super-dense coding and quantum teleportation. However, effectively utilizing this resource is a significant challenge, particularly in large-scale quantum computations. Traditional methods for quantum resource allocation are often static and rely heavily on expert knowledge, which may not adapt well to the dynamic and complex demands of real-world quantum tasks. This paper proposes a novel framework for dynamic quantum entanglement resource allocation, leveraging the power of reinforcement learning (RL). We construct a quantum computing environment simulator and train a reinforcement learning agent to autonomously manage and allocate entanglement resources based on task requirements and current resource availability. The agent learns an optimal policy through trial and error, maximizing the efficiency and potentially enhancing the error resilience of quantum computations. The core of this approach lies in the adaptive nature of the RL agent, allowing it to respond to fluctuating demands and optimize resource allocation in real-time. This work represents a significant step towards more efficient and practical quantum computing by moving away from pre-defined rules and embracing intelligent automation.
Quantum computing has been combined with deep learning, and this has created new opportunities to accelerate dataintensive applications and add more computational intelligence. Quantum-Assisted Deep Learning (QADL) uses the capability of quantum parallelism to address the limits to training and scaling of conventional neural networks. But applied to the high-performance cloud infrastructures (HPCIs), QADL experiences some challenges, including quantum-classical synchronization delays, inefficient resource usage, and weak scalability to dynamic workloads. In the attempt to overcome these issues, it is proposed in this paper (QADROT), Quantum-Assisted Deep Reinforcement Optimization of Intelligent Training, a new optimization model of effective hybrid computing. The Q-ADROIT uses a quantum-controlled reinforcement learning to allocate computational tasks in both quantum and classical computers dynamically. It combines an adaptive entanglement-based scheduler to reduce the data transfer latency and a hyperparameter tuner that is self-optimizing and uses quantum sampling to enhance convergence and accuracy. Empirical investigations using benchmark data demonstrate that Q-ADROIT outperforms the current models by 41 percent improvement in training speed, a 29 percent decrease in latency and predictive accuracy. In general, Q-ADROIT can greatly enhance the efficiency, scalability and energy optimization of the QADL systems in cloud infrastructures.
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework utilizing DRL to automate this process, learning optimal strategies for allocating quantum resources (qubits, gates, and time) to maximize the performance of quantum algorithms. The core of our approach involves constructing a DRL model where the agent learns to make decisions regarding resource allocation based on the current state of the quantum system and the desired algorithm outcome. We define a state space representing the quantum system's configuration, action space encompassing various resource allocation options, and a reward function that reflects the algorithm's success. Through this framework, we aim to significantly enhance the efficiency and performance of quantum computations. This work presents a foundational approach, paving the way for more sophisticated DRL-based quantum resource allocation strategies.
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.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.