##基于非冯诺依曼架构的量子计算资源调度

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22157217

Related papers

Non-Von Neumann Architecture Quantum Computing Resource Allocation

Jincheng Zhang

This paper addresses the critical challenge of resource allocation in quantum computing, moving beyond the constraints of traditional Von Neumann architectures. The core argument centers on designing a dynamic resource allocation mechanism leveraging the inherent properties of quantum mechanics – superposition and entanglement – to maximize resource utilization and computational efficiency. This is achieved through the introduction of quantum resource entropy, a novel metric for quantifying resource utilization. We propose a quantum resource allocation algorithm predicated on this entropy value, facilitating dynamic optimization of quantum resources. The novelty lies in recognizing the fundamental differences between quantum and classical computing and adapting resource management strategies accordingly. The proposed approach offers a potential pathway towards more efficient and effective quantum computation by intelligently responding to the fluctuating demands of quantum algorithms. The research explores the theoretical underpinnings of this dynamic allocation and suggests future directions for development and implementation.

Sep 7, 20260 citations

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

基于自适应高斯分布的量子计算资源调度算法

Jincheng Zhang

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.

Sep 5, 20260 citations

Quantum Computation Resource Scheduling and Optimization Theory

Jincheng Zhang

This paper presents a novel theoretical framework for resource scheduling and optimization within quantum computing. The core claim is to establish a method capable of optimally allocating quantum resources based on the specific characteristics of quantum programs and the constraints imposed by hardware limitations. The proposed approach leverages linear programming and combinatorial optimization techniques to formulate a quantum computation resource scheduling model. An algorithm is designed to optimize this model, ultimately leading to efficient resource utilization. The research addresses a complex and evolving area of quantum computing, aiming to provide a foundational theory for practical quantum resource management. The key contribution lies in the development of a tailored optimization model that considers the unique demands of quantum algorithms and the inherent limitations of quantum hardware. This work lays the groundwork for future advancements in quantum computation by offering a systematic approach to resource allocation.

Aug 29, 20260 citations

Non-Von Neumann Architectures for Quantum Computing Hardware

Jincheng Zhang

This paper proposes a novel approach to quantum computing hardware that diverges from the traditional Von Neumann architecture. The core argument centers around leveraging quantum entanglement and superposition for inherent parallel computation, addressing limitations inherent in the conventional model. We introduce a heterogeneous quantum computing architecture built upon quantum bits (qubits) and quantum circuits, designed to facilitate data processing and control through orchestrated quantum operations. The fundamental innovation lies in moving away from the sequential processing paradigm dictated by Von Neumann architecture, offering a potentially more efficient hardware platform for quantum computations. This architecture aims to unlock significant computational speedups by exploiting the quantum mechanical properties of superposition and entanglement, moving beyond the constraints of classical bit manipulation. The design incorporates a novel control system and qubit connectivity model to maximize computational parallelism. This research contributes to the exploration of alternative hardware designs critical for the advancement of scalable and performant quantum computing systems.

Sep 3, 20260 citations

Quantum State Superposition-Based Parallel Computing Resource Scheduling

Jincheng Zhang

This paper investigates a novel approach to parallel computing resource scheduling leveraging the principles of quantum state superposition. Traditional parallel computing methods face limitations in efficiently utilizing available resources due to inherent sequential execution constraints. We propose a framework that maps computational tasks onto quantum state superposition, enabling parallel execution through quantum control techniques. Furthermore, we explore the potential of quantum entanglement for optimizing resource allocation and scheduling. This research addresses the core claim of enhancing computational efficiency by exploiting the unique properties of quantum mechanics. The methodology presented offers a fundamentally different paradigm for resource management, potentially unlocking significant performance improvements in computationally intensive applications. This work introduces a new method for parallel computation resource scheduling by utilizing quantum state superposition. The key mechanism involves mapping tasks to superposition states, parallel execution via quantum control, and entanglement-based resource scheduling.

Sep 3, 20260 citations