基于自适应高斯分布的量子计算资源调度算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum computing is a rapidly advancing field with the potential to revolutionize numerous industries. However, the practical implementation of quantum computers faces significant challenges, particularly in resource allocation. Efficient resource allocation is crucial for realizing the full potential of quantum computing. This paper explores a novel quantum computing resource scheduling algorithm based on adaptive Gaussian distributions, aiming to optimize resource allocation and improve overall quantum computation efficiency. The core mechanism involves dynamically adjusting the parameters of the Gaussian distribution to reflect the evolving requirements of quantum resources. This approach offers a more flexible and adaptive solution compared to traditional static scheduling methods, addressing a key bottleneck in quantum computing resource management. We detail the design, implementation, and evaluation of this algorithm, highlighting its potential advantages and limitations.

DOI: 10.5281/zenodo.22316854

Related papers

Quantum Computing Resource Allocation and Scheduling Algorithm Design

Jincheng Zhang

This paper presents a novel approach to resource allocation and scheduling within quantum computing environments. Recognizing the inherent scarcity and unique characteristics of quantum resources, such as qubits and quantum gates, we propose a dynamic algorithm designed to optimize resource utilization and consequently enhance computational efficiency. The core of the algorithm lies in a mechanism that intelligently balances the demands of diverse quantum computing tasks. We formalize this approach using mathematical notation, detailing the key components including task prioritization, qubit assignment, gate scheduling, and dynamic adjustment based on real-time system metrics. The proposed algorithm addresses limitations within existing static scheduling methods, offering improved performance in complex quantum workflows. The design incorporates a queuing theory-inspired model to manage task flow and a stochastic optimization technique for adaptive resource allocation. Simulation results (presented conceptually here) demonstrate the algorithm's potential to significantly improve quantum computing performance compared to traditional approaches. Further research will focus on formal verification and optimization for specific quantum computing architectures.

Sep 2, 20260 citations

Dynamic Quantum Computing Resource Allocation and Scheduling Algorithm

Jincheng Zhang

This paper presents a novel approach to dynamic quantum computing resource allocation and scheduling, addressing the limitations of static resource management in the field. The core claim is that real-time monitoring of quantum resource utilization combined with the inherent characteristics of quantum algorithms can lead to optimized dynamic allocation and scheduling, ultimately boosting quantum computing efficiency. The proposed mechanism leverages reinforcement learning (RL) to learn an optimal resource allocation policy. The RL agent adapts to the specific requirements of quantum algorithms, considering factors such as the number of qubits and their entanglement level. The algorithm aims to minimize execution time and maximize resource utilization. This research contributes to a more flexible and efficient utilization of scarce quantum computing resources, a critical factor in the advancement of quantum computation. The key performance indicator is the reduction in the average execution time of quantum algorithms, compared to static allocation strategies. The theoretical framework is built upon queuing theory and stochastic optimization techniques.

Sep 4, 20260 citations

##基于非冯诺依曼架构的量子计算资源调度

Jincheng Zhang

This paper presents a novel resource scheduling system for quantum computing based on a non-Von Neumann architecture. Traditional Von Neumann architectures face significant bottlenecks when applied to quantum computing due to the inherent parallelism and probabilistic nature of quantum processes. This system addresses these challenges by explicitly recognizing and leveraging the core characteristics of quantum computation – the decomposition of tasks into quantum states, quantum gates, and measurements – and the dynamic interactions between these states. The core innovation lies in the introduction of a Quantum State Graph (QSG) to represent the execution flow of quantum computations. The QSG provides a semantic framework for understanding and managing the complex relationships within a quantum algorithm. This allows for dynamic allocation and optimization of quantum resources, moving beyond the limitations of sequential, state-based scheduling. The proposed methodology aims to improve the efficiency and scalability of quantum computing by aligning resource management with the quantum mechanical principles at its foundation. The system is designed to be adaptable and robust, capable of handling the inherent uncertainty in quantum computations. The paper outlines the system architecture, the QSG representation, and the scheduling algorithm, highlighting the key benefits and potential future directions.

Aug 29, 20260 citations

基于自组织网络的量子算法优化

Jincheng Zhang

Quantum computing is poised to revolutionize numerous fields, offering unparalleled computational power. However, scaling up quantum computers faces significant challenges, particularly concerning algorithm efficiency. This paper explores the application of a novel quantum algorithm optimization framework based on self-organizing networks. We propose a dynamic allocation strategy leveraging these networks to optimize quantum algorithms, aiming to enhance their performance and scalability. The core mechanism focuses on structuring the quantum system using self-organizing networks, enabling efficient resource allocation and reducing computational complexity. This work presents a framework demonstrating significant improvements in quantum algorithm execution speed compared to existing optimization techniques. The potential impact of this approach is substantial, paving the way for practical quantum computation. This research contributes to the advancement of quantum algorithm design and provides a foundation for exploring the full potential of self-organizing networks in quantum computing.

Aug 31, 20260 citations

基于自组织网络的自适应量子计算调度

Jincheng Zhang

Quantum computing, a revolutionary field, promises unprecedented computational power. However, efficient resource allocation remains a significant challenge. This paper introduces a novel algorithm, "基于自组织网络的自适应量子计算调度", which dynamically adjusts the scheduling of quantum computation tasks based on a self-organizing network. This approach aims to optimize resource utilization and significantly enhance the efficiency of quantum computations. The core mechanism leverages the inherent structure of self-organizing networks to create a flexible and adaptive scheduling system. We present a theoretical analysis and empirical validation demonstrating the algorithm's effectiveness in improving task allocation and reducing computational time. The potential benefits of this approach are substantial, paving the way for broader adoption of quantum computing.

Sep 7, 20260 citations

Adaptive Quantum Algorithm Parallel Expansion Strategy

Jincheng Zhang

This paper introduces an adaptive quantum algorithm parallel expansion strategy designed to optimize computational performance in quantum computing environments. The core of this approach lies in dynamically adjusting the parallelism of quantum computations based on the allocation of available quantum resources. We propose a novel mechanism that utilizes quantum resource allocation information to automatically scale the complexity of parallel computations, ensuring efficient execution. The proposed strategy offers a significant advantage over static expansion techniques, providing a flexible and adaptable solution for tackling complex quantum problems. This work explores the benefits of dynamic resource allocation in quantum algorithm development, demonstrating a practical implementation and analysis of the proposed method.

Sep 3, 20260 citations