Title: Adaptive Quantum Machine Learning for Signal Processing

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of adaptive quantum machine learning (AQML) to signal processing, focusing on the development of a novel algorithm capable of automatically adjusting its parameters based on the characteristics of the input signal. The core mechanism leverages quantum optimization to iteratively refine the ML model, enhancing robustness and achieving superior performance compared to traditional methods. The research explores the potential of quantum optimization to address the inherent challenges of noisy data in signal analysis. This work presents a framework for dynamically adjusting model parameters to minimize errors and improve the overall quality of the processed signal.

DOI: 10.5281/zenodo.22277070

Related papers

Title: Quantum Machine Learning for Dynamic System Modeling: Adaptive Parameter Adjustment

Jincheng Zhang

This paper presents a novel approach to dynamic system modeling utilizing quantum machine learning (QML) for adaptive parameter adjustment. Traditional system modeling often relies on fixed parameters, limiting the model's ability to accurately represent complex dynamics. We propose a QML framework where the system is treated as a dynamic process, and the algorithm dynamically adjusts parameters to enhance the representation of system behavior. This approach offers enhanced precision and adaptability compared to conventional methods. The core mechanism involves employing a quantum reinforcement learning algorithm to iteratively refine the model's parameters, leading to improved predictive capabilities. This research explores the potential of QML to overcome limitations inherent in traditional system modeling and provides a foundation for future advancements in dynamic system analysis.

Sep 4, 20260 citations

Title: Quantum Adaptive Machine Learning Model Optimization

Jincheng Zhang

This paper investigates the application of quantum self-adaptation to machine learning model optimization. Traditional optimization methods often rely on static parameter adjustments, limiting the potential for improved model performance. We propose a novel framework leveraging quantum self-adaptation to dynamically adjust machine learning model parameters, enhancing both predictive accuracy and generalization ability. The core mechanism involves quantum-enhanced parameter adjustment, enabling the model to adapt to complex data patterns and avoid local optima. This research explores the potential of quantum self-adaptation to significantly improve the efficiency and effectiveness of machine learning algorithms. This work presents a theoretical analysis and preliminary experimental validation of the proposed framework, demonstrating its effectiveness in optimizing various machine learning models.

Aug 31, 20260 citations

Title: Adaptive Quantum Field Theory for Signal Processing

Jincheng Zhang

Quantum field theory (QFT) offers a fundamentally different perspective on signal processing compared to traditional methods. This research explores the development of an Adaptive Quantum Field Theory (AQFT) designed to dynamically adjust its parameters based on the input signal, thereby enhancing detection capabilities. The core mechanism utilizes a variational quantum algorithm to optimize the field parameters in real-time, ensuring optimal performance across a range of signal characteristics. The proposed AQFT represents a novel approach to signal analysis, addressing limitations inherent in classical methods through a dynamically responsive and robust system. This work presents a conceptual framework with potential for significant advancements in precision and adaptability within signal processing.

Sep 4, 20260 citations

Title: Adaptive Topology-Based Quantum Machine Learning

Jincheng Zhang

Quantum machine learning (QML) is a rapidly developing field with the potential to revolutionize various computational tasks. This paper introduces an innovative approach to QML, leveraging adaptive topology to dynamically adjust machine learning model parameters. We propose a novel framework that constructs a quantum machine learning model based on the concept of quantum geometry, allowing it to adapt to the inherent structure of input data. The core mechanism centers around a quantumization process that maps input data into a dynamically evolving topological structure. This transformation enables the model to learn effectively from complex, non-linear data. This work addresses a critical gap in QML by incorporating geometric transformations, aiming to enhance predictive accuracy compared to traditional approaches. The proposed framework demonstrates promising results through theoretical analysis and preliminary experimental validation, suggesting a significant advancement in the capabilities of QML.

Sep 7, 20260 citations

Quantum Machine Learning: Adaptive Optimization Algorithms

Jincheng Zhang

Quantum Machine Learning (QML) is an emerging field that explores the potential of quantum computing to revolutionize various machine learning tasks. This paper investigates a novel adaptive optimization algorithm specifically tailored for QML, focusing on dynamically adjusting quantum parameter values to enhance model robustness and performance in the face of noisy training data. The core mechanism leverages the inherent self-adaptation capabilities of quantum machine learning to optimize the quantum system's parameters in response to the data distribution. We present a detailed analysis of the algorithm's design, its potential benefits, and its practical implications for advancing QML research.

Aug 31, 20260 citations

Title: Adaptive Quantum Algorithm Optimization via Dynamic Quantum Algorithm Tuning

Jincheng Zhang

This paper presents a novel optimization approach for quantum algorithms leveraging dynamic quantum algorithm tuning. Traditional optimization methods often rely on static parameter settings, limiting the potential for efficient algorithm execution. We introduce a dynamic mechanism that automatically adjusts the quantum algorithm's execution characteristics based on learned properties, resulting in accelerated quantum computation. The core of this method lies in a "dynamic quantum algorithm tuning" mechanism, which iteratively refines the algorithm's parameters to achieve optimal performance. This approach addresses the limitations of static optimization techniques by providing a flexible and adaptive optimization strategy. This research explores the potential of dynamically adjusting quantum algorithms to significantly enhance their computational speed.

Sep 3, 20260 citations