Quantum Simulation of Self-Adaptive Gradient Descent

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum simulation offers unprecedented capabilities for studying complex quantum systems, but traditional methods often struggle with computational complexity and require significant parameter adjustments. This paper introduces a novel quantum simulation approach utilizing self-adaptive gradient descent, a technique designed to dynamically adjust simulation parameters based on observed results. This algorithm significantly enhances simulation accuracy and efficiency by intelligently optimizing the simulation process. We present a framework that allows for continuous parameter refinement, mitigating the limitations of static parameter settings. The core mechanism centers around a learned parameter adjustment strategy, leveraging the inherent dynamics of the quantum system to improve the simulation. This work demonstrates the effectiveness of this approach through theoretical analysis and preliminary simulations, showcasing a substantial improvement in simulation fidelity compared to existing methods. The findings highlight the potential of self-adaptive gradient descent for accelerating and refining quantum simulations.

DOI: 10.5281/zenodo.22265727

Related papers

Quantum Machine Learning: A Self-Adaptive Gradient Descent Algorithm

Jincheng Zhang

Quantum Machine Learning has emerged as a promising field, offering the potential to accelerate machine learning algorithms and enhance their capabilities. This paper introduces a novel quantum machine learning algorithm, termed "Self-Adaptive Gradient Descent," that leverages the principles of quantum mechanics to dynamically adjust gradient descent parameters, enabling more efficient and accurate learning of complex neural networks. The core mechanism involves a quantum-enhanced gradient descent process, optimizing the parameters based on the data's inherent dynamics. We demonstrate the effectiveness of this algorithm through a series of experiments on a benchmark dataset, showcasing improved accuracy and faster convergence compared to traditional gradient descent methods. The core claim is that this algorithm can effectively learn complex neural networks and achieve more precise predictions.

Sep 6, 20260 citations

Adaptive Parameter Adjustment for Quantum Simulation

Jincheng Zhang

This paper introduces an adaptive parameter adjustment framework for quantum simulation, aiming to accelerate the computation and enhance the accuracy of simulation results. Traditional quantum simulation methods often struggle with complex problems due to static parameter settings. Our approach dynamically adjusts these parameters during the simulation process to optimize the results, offering a significant advancement over static methods. The core mechanism involves a continuous feedback loop, iteratively refining the parameters based on the observed simulation outcomes. The paper details the framework's design, implementation, and preliminary results demonstrating improved efficiency and accuracy in a specific quantum simulation task.

Aug 31, 20260 citations

Adaptive Quantum Computing Quantum Simulation Optimization

Jincheng Zhang

This paper investigates the application of a self-adaptive quantum simulation optimization algorithm to enhance the accuracy and efficiency of quantum simulations. Traditional simulation methods often rely on fixed parameters, limiting the potential for optimization. This research proposes a novel algorithm that dynamically adjusts simulation parameters based on simulation results, resulting in continuous refinement and improved performance. The core mechanism focuses on a reinforcement learning-inspired approach to optimize the simulation process. This adaptive strategy offers a significant advancement in quantum simulation, addressing limitations of static optimization techniques and promising improved results across a range of quantum systems. The paper details the algorithm's design, implementation, and results, demonstrating its potential for enhancing the capabilities of quantum simulations.

Aug 31, 20260 citations

基于自适应量子模拟器设计

Jincheng Zhang

This paper presents a novel quantum simulation framework centered around adaptive quantum simulation, designed to enhance the efficiency and effectiveness of quantum simulations. Traditional quantum simulation methods often struggle with complex, high-dimensional systems due to the computational cost associated with parameter tuning. Our approach addresses this challenge by incorporating a dynamic adjustment mechanism that intelligently modifies simulation parameters based on the system's state, offering a significant improvement over static parameter settings. The proposed framework leverages a layered approach, beginning with a foundational quantum simulator and progressively refining its behavior through a self-adaptive process. This allows for a more nuanced and responsive simulation, ultimately enabling the resolution of intricate quantum systems and facilitating the exploration of previously intractable problems. The paper details the architecture, implementation considerations, and preliminary experimental results demonstrating the framework's effectiveness. The core claim of this work is that the proposed adaptive quantum simulator framework offers a substantial improvement in simulation efficiency and allows for the exploration of complex quantum systems.

Aug 29, 20260 citations

Quantum Adaptive Algorithm Parameter Optimization Strategies

Jincheng Zhang

Quantum adaptive algorithms represent a significant advancement in the field of quantum computing, offering the potential to accelerate complex computations by dynamically adjusting quantum parameters. This paper investigates a novel parameter optimization strategy based on the principles of quantum self-adaptivity, aiming to enhance the performance of existing quantum algorithms. We propose a method that iteratively adjusts quantum state parameters based on feedback from the algorithm's execution, leveraging the inherent capabilities of quantum self-adaptivity to achieve optimal performance. This approach distinguishes itself through a dynamic parameter adjustment cycle that continuously refines the quantum system's state, leading to improved results. The paper details the core mechanism, the algorithm's design, and the experimental validation demonstrating its effectiveness. This work presents a promising path towards more efficient quantum computation.

Sep 6, 20260 citations

Self-Adaptive Quantum State Simulation Parameter Optimization

Jincheng Zhang

This paper presents a novel framework for quantum state simulation parameter optimization, driven by a self-adaptive module. Traditional quantum simulation often relies on manual parameter tuning, limiting the ability to effectively model complex quantum phenomena. Our approach introduces a dynamically adjusting parameter optimization module that leverages learned quantum state characteristics to refine simulation results. This leads to improved accuracy and efficiency in simulating quantum systems. The framework addresses a significant challenge in quantum simulation – the need for automated parameter adjustment – and offers a promising solution for tackling increasingly complex problems. The core mechanism centers around a self-learning algorithm that adapts the simulation parameters to best match the observed quantum state evolution. This results in a more robust and accurate simulation process.

Aug 31, 20260 citations