A Neuromorphic Computing-Based Accelerator for Quantum Algorithm Acceleration

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the design and potential of a neuromorphic computing-based accelerator for quantum algorithms. The core idea is to leverage the inherent parallelism of neuromorphic hardware to significantly speed up the computationally intensive aspects of quantum algorithms. We propose a methodology for decomposing complex quantum algorithms into a set of parallelizable tasks, which are then mapped onto a neuromorphic architecture. The architecture utilizes the key characteristics of neuromorphic systems—namely, synaptic plasticity and dynamic connection adjustment—to optimize the execution of these quantum tasks. This approach aims to address the significant computational bottleneck currently limiting the practical application of many quantum algorithms. The design incorporates key mathematical concepts from quantum mechanics and neuromorphic computing, providing a framework for future research and development in this emerging field. The paper outlines the architectural considerations, the mapping strategies, and discusses the potential for achieving substantial performance gains. ---

DOI: 10.5281/zenodo.22613893

Related papers

Adaptive Quantum Entanglement Computation Based on Neuromorphic Networks

Jincheng Zhang

This paper proposes a novel computational paradigm leveraging the inherent properties of neuromorphic networks and quantum entanglement. The core claim is to achieve adaptive and efficient parallelization of computational tasks by dynamically adapting the architecture and resource allocation based on entanglement characteristics. The proposed mechanism utilizes a neuromorphic network where connection weights are modulated by the strength and topology of quantum entanglement. Real-time adjustments are performed based on input data and the current computational state, facilitating parallel quantum computation and dynamic resource allocation. This approach overcomes limitations inherent in traditional neuromorphic networks and conventional quantum computing, offering a potentially transformative solution for complex computational problems. The system's adaptability offers a pathway toward scalable quantum computation, where entanglement resources can be optimized for specific tasks. This research explores the theoretical framework and key design considerations for such a system, highlighting its potential impact on diverse fields. ---

Sep 4, 20260 citations

Quantum Algorithm Acceleration Based on Biological Neural Networks

Jincheng Zhang

This paper proposes a novel approach to quantum algorithm acceleration by leveraging the inherent parallelism of biological neural networks. The core idea is to construct a hardware platform that mimics the architecture and computational properties of biological neural networks to serve as an accelerator for quantum algorithms. This approach aims to overcome the limitations of traditional quantum computing architectures, which often suffer from bottlenecks in qubit control and communication. We outline a framework for designing such an accelerator, focusing on mapping quantum algorithms onto biologically inspired parallel processing structures. The potential benefits of this hybrid approach – combining the computational strengths of both biological and quantum systems – are significant, potentially leading to substantial improvements in quantum computing performance and efficiency. This work establishes a theoretical foundation and proposes a novel direction for quantum computing research, focusing on bio-inspired hardware solutions.

Sep 1, 20260 citations

Hardware Acceleration Algorithms Based on Quantum Mechanics Principles

Jincheng Zhang

This paper explores the design and implementation of hardware acceleration algorithms leveraging the principles of quantum mechanics. The core strategy involves mapping computationally intensive algorithms onto quantum computers, harnessing quantum phenomena like superposition and interference to achieve significant speedups. We delve into the selection of appropriate algorithms, their decomposition into quantum circuits, and the execution of these circuits on quantum hardware. The research highlights the potential of quantum computing to tackle problems intractable for classical computers. The presented approach offers a novel pathway to computational acceleration, driven by fundamental quantum mechanical concepts. This work contributes to the growing field of quantum algorithm design and explores the practical considerations of utilizing quantum hardware for accelerating specific computational tasks. ---

Sep 2, 20260 citations

Neuro-Morphic Quantum Computing: A Hardware Architecture Exploration

Jincheng Zhang

This paper explores a novel hardware architecture for neuro-morphic quantum computing, aiming to leverage the strengths of both quantum and neuromorphic computing paradigms for enhanced parallel processing. The proposed architecture integrates quantum bits (qubits) with a neural network, utilizing the inherent parallelism of quantum computation and the adaptive learning capabilities of neuromorphic systems. This work outlines the core design principles and theoretical foundations, focusing on the efficient interaction between these two distinct computational modalities. The architecture's potential to significantly improve computational efficiency and scalability is discussed. The key elements include a quantum processing unit (QPU) coupled to a spiking neural network (SNN) via a carefully designed interface. The theoretical framework examines the impact of quantum entanglement and neural network plasticity on the overall computation. Further research directions are proposed to refine and validate this architecture.

Sep 2, 20260 citations

Quantum Accelerated Computation via Entangled Teleportation

Jincheng Zhang

This paper presents a novel approach to hardware acceleration of specific computational tasks leveraging the principles of quantum entanglement and quantum teleportation. The core idea is to decompose complex calculations into a series of entangled qubit operations, facilitated by quantum teleportation, thereby achieving significant speedups. We outline a framework for designing and implementing such accelerators, focusing on the efficiency and scalability of this method. The proposed architecture aims to bypass traditional computational bottlenecks by directly exploiting the inherent parallelism of quantum mechanics. This work establishes a theoretical foundation for quantum teleportation-based hardware acceleration, offering a promising direction for future quantum computing advancements. The key metrics for evaluating performance – speedup and fidelity – are explicitly discussed. ---

Aug 28, 20260 citations

Based on Quantum Mechanics Principles Hardware Accelerator Design

Jincheng Zhang

This paper presents the design of a novel hardware accelerator leveraging fundamental principles of quantum mechanics. The core concept involves translating specific computational algorithms into quantum circuits and executing them on quantum hardware platforms such as superconducting qubits or ion traps. This approach aims to achieve significant speedup compared to classical algorithms. The design focuses on optimizing the quantum circuit for targeted algorithms, considering factors like qubit connectivity, gate fidelity, and measurement schemes. This work outlines the theoretical framework, key design considerations, and potential performance gains anticipated from utilizing quantum mechanical phenomena like superposition and entanglement for hardware acceleration. The primary goal is to demonstrate the feasibility and potential of quantum-inspired hardware accelerators for solving computationally intensive problems. ---

Aug 29, 20260 citations