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Title: Dynamically Generated Periodic Boundary Conditions for Quantum Simulation

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper presents a novel method for generating dynamically adjusted periodic boundary conditions (PBCs) within quantum simulation frameworks. Current approaches often introduce systematic errors that can significantly degrade the fidelity of simulation results. We introduce a dynamic PBC generation algorithm that intelligently adapts the boundary conditions based on the simulation's progress, effectively mitigating these artifacts. This approach offers a significant improvement in simulation accuracy and robustness, representing a substantial advancement in the field of quantum simulation. The core mechanism focuses on a self-regulating system that minimizes errors through iterative adjustment. The goal is to develop a practical and effective technique for enhancing the quality of quantum simulations.

DOI
10.5281/zenodo.22270275

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Quantum algorithms, renowned for their potential to solve complex problems intractable for classical computers, rely heavily on precise parameter control. Traditional parameter optimization methods, often based on iterative refinement, can be computationally expensive and may not always converge to optimal solutions. This paper introduces a novel dynamic parameter adjustment approach for quantum algorithms, aiming to enhance algorithm efficiency and accuracy by autonomously adapting to the evolving quantum state. We propose a method that employs a feedback loop, dynamically adjusting parameters based on the observed quantum state, thereby mitigating the effects of data changes and improving the overall algorithm performance. The core mechanism lies in leveraging a continuous, self-regulating parameter update strategy driven by the quantum algorithm's trajectory. This approach offers a significant advancement over existing optimization techniques by providing a more adaptive and robust solution.
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Quantum simulation of condensed matter systems offers unprecedented insights into their behavior, but current methods often struggle to accurately represent the complex dynamics and intricate topologies. This paper introduces an adaptive topology framework for quantum simulation, aiming to overcome these limitations. We propose a novel algorithm that dynamically adjusts the topology of the quantum simulation space during the simulation process, guided by the system's dynamics. This adaptation significantly improves the representation of the underlying physics, leading to enhanced accuracy and robustness. The core mechanism leverages a feedback loop that continuously evaluates the topology and refines it to achieve optimal representation. This approach promises a significant advancement in quantum simulation capabilities, particularly for complex systems exhibiting intricate topologies.
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