Title: Modular Quantum Computation with Adaptive Resource Allocation (MQAR)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum computation holds immense promise for scientific discovery and technological advancement, but current implementations face significant challenges related to scalability and algorithm efficiency. This paper introduces Modular Quantum Computation with Adaptive Resource Allocation (MQAR), a novel architecture designed to overcome these limitations by dynamically allocating quantum resources across modules. MQAR utilizes a resource allocation planner to intelligently distribute qubits, entanglement, and other essential resources, optimizing performance through massively parallel execution of complex algorithms. We detail the design of the resource allocation planner, its implementation, and its impact on the execution of a complex quantum algorithm, demonstrating the benefits of this approach in terms of throughput and resource utilization. The focus is on enabling efficient execution of complex algorithms, particularly those requiring fine-grained control and adaptability.

DOI: 10.5281/zenodo.22300072

Related papers

Title: Modular Quantum Computation via Entangled Resource Allocation

Jincheng Zhang

This paper explores the potential of modular quantum computation, leveraging entangled resource allocation to achieve unprecedented parallelism. Traditional quantum algorithms often struggle with limited computational resources, hindering their scalability. We propose a novel architecture centered on the concept of a 'Quantum Resource Pool' – a dynamically adjusted collection of entangled qubits – which distributes computational tasks across the pool, governed by a feedback loop. This approach aims to overcome the limitations of existing quantum algorithms by effectively exploiting the inherent correlations within entangled states. The paper details the design of the resource pool, the feedback mechanism, and a preliminary analysis of its potential benefits for quantum computation.

Sep 3, 20260 citations

Title: Adaptive Quantum Circuit Design with Dynamic Resource Allocation

Jincheng Zhang

Quantum circuit design presents a significant challenge in the field of quantum computing, demanding efficient and robust circuit layouts. Static circuit layouts are often limited by the inherent complexity of quantum systems and can hinder optimization for both fidelity and speed. This research explores a novel adaptive quantum circuit design approach utilizing dynamic resource allocation. We propose a system that dynamically allocates quantum resources (qubits, gates) during circuit construction, optimizing for both fidelity and speed. The proposed framework leverages a 'resource allocation scheduling' algorithm that intelligently manages resources based on problem complexity and current system state. This system aims to address a key bottleneck in quantum circuit design – moving beyond static layouts to adaptive resource allocation, potentially unlocking significant improvements in both circuit fidelity and computational speed. This work presents a complete theoretical and practical implementation of this approach, demonstrating its effectiveness through simulation and preliminary experimentation. The focus is on creating a system that can intelligently manage the resources required for quantum circuit construction, leading to more efficient and robust designs.

Sep 2, 20260 citations

Title: Adaptive Quantum Error Correction with Dynamic Resource Allocation

Jincheng Zhang

Quantum computing holds immense promise for revolutionary advancements across numerous fields, but the inherent fragility of quantum information necessitates sophisticated error correction techniques. Current quantum error correction methods often rely on computationally intensive overhead, limiting scalability. This paper proposes an adaptive quantum error correction scheme that dynamically allocates resources to different parts of the quantum circuit based on the observed error rate. We introduce a novel resource-aware algorithm that adjusts the amount of error correction applied to each qubit based on its current error state, aiming to significantly improve error correction efficiency and reduce the overall complexity of quantum computations. This work addresses a critical challenge in quantum computing – the need for efficient error correction – with a mechanism for adaptive resource allocation. The proposed algorithm demonstrates substantial improvements in error correction performance compared to existing approaches in initial simulations and preliminary experiments. We conclude with a discussion of the potential implications of this approach for future quantum systems.

Sep 2, 20260 citations

Title: Adaptive Quantum Entanglement Optimization for Quantum Algorithms

Jincheng Zhang

Quantum algorithms hold immense promise for scientific discovery and technological advancement, but their practical implementation often faces significant challenges related to efficiency and accuracy. This paper presents a novel quantum algorithm optimization framework based on the concept of adaptive quantum entanglement. We propose a "Quantum Entanglement Parameter Optimizer" (QEPO) that dynamically adjusts entanglement parameters to enhance the performance of quantum algorithms, particularly in the domains of quantum simulation and quantum computation. The core mechanism of the QEPO leverages the inherent properties of quantum entanglement to automatically optimize these parameters, leading to improved results. The study demonstrates the effectiveness of this approach through simulations and preliminary experiments, highlighting its potential to revolutionize quantum algorithm optimization.

Aug 31, 20260 citations

Title: Adaptive High-Order Quantum Computation

Jincheng Zhang

This paper investigates the development of an adaptive high-order quantum computation framework designed to accelerate complex computations through the strategic utilization of quantum entanglement and dynamic parameter adjustment. Traditional quantum algorithms often struggle to achieve high-order solutions, presenting a significant challenge for computationally demanding problems. This research proposes a novel approach that dynamically adjusts quantum computation parameters to optimize performance, offering a promising solution for tackling intricate challenges. The core mechanism centers around a self-optimizing quantum algorithm, enabling efficient execution of intricate calculations. This work explores the potential of this framework to address complex problems across various scientific and engineering domains.

Sep 4, 20260 citations

Title: Adaptive Geometric Transformations for Quantum Computing

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, from drug discovery to materials science. However, realizing this potential requires efficient and scalable quantum algorithms. This paper explores the application of adaptive geometric transformations as a core mechanism for enhancing the performance of quantum computations. We propose a novel algorithm that dynamically adjusts geometric parameters during the computation process, aiming to achieve higher fidelity and reduced computational complexity. The core claim is that this approach significantly improves the efficiency of quantum algorithms, particularly in scenarios involving complex quantum circuits. The paper details the design of the adaptive transformation function, its implementation within a quantum computing framework, and a preliminary analysis of its impact on key quantum algorithms. The study demonstrates the efficacy of this approach through simulations and a simplified demonstration of a specific quantum circuit. The research contributes to the ongoing development of robust and adaptable quantum algorithms.

Sep 3, 20260 citations