Quantum Machine Learning via Probability Perturbation Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Machine Learning via Probability Perturbation Algorithm is a novel approach to machine learning that integrates quantum computing and probability perturbation techniques. This research explores the potential of leveraging quantum phenomena like superposition and entanglement to enhance model training and generalization capabilities, particularly for high-dimensional datasets. The core mechanism involves introducing controlled, probabilistic perturbations to the model's parameters during the training process, designed to nudge the learning landscape towards more robust and generalizable solutions. This paper outlines the theoretical foundation, experimental setup, and initial results demonstrating the efficacy of this algorithmic framework. We aim to present a promising avenue for advancing machine learning in complex, high-dimensional environments.

DOI: 10.5281/zenodo.22280945

Related papers

Quantum Machine Learning Algorithm Optimization

Jincheng Zhang

Quantum Machine Learning Algorithm Optimization presents a novel approach to accelerate machine learning model training and optimization through the strategic application of quantum algorithms. This research explores the potential of leveraging quantum phenomena, such as superposition and entanglement, to enhance the efficiency of computationally intensive machine learning tasks. The core mechanism focuses on designing and implementing quantum optimization algorithms specifically tailored to address the unique challenges posed by classical machine learning models. This paper outlines a framework for evaluating the efficacy of such algorithms, highlighting key theoretical foundations and preliminary experimental results demonstrating significant improvements in training speed and parameter space exploration.

Sep 4, 20260 citations

Quantum-Based Probabilistic Model Learning

Jincheng Zhang

This paper explores the potential of quantum machine learning (QML) for efficient and accurate probabilistic model learning. Traditional machine learning algorithms often struggle with computational complexity and scalability, particularly when dealing with high-dimensional data and complex probability distributions. This research proposes a novel approach leveraging principles from quantum information theory to design a QML algorithm capable of learning probabilistic models. The core mechanism involves representing model parameters as quantum states and utilizing quantum measurements and entanglement to perform parameter estimation. We aim to demonstrate that this quantum-based method can overcome limitations inherent in classical approaches, potentially leading to significant improvements in both computational efficiency and predictive accuracy. This work presents a foundational framework for further research and development in this emerging field.

Aug 29, 20260 citations

Dynamic Quantum Entanglement for Probabilistic Computation

Jincheng Zhang

Quantum entanglement, a fundamental quantum phenomenon, offers a unique pathway toward computational advancements. This paper explores the integration of dynamic quantum entanglement into probabilistic computation, aiming to enhance accuracy and efficiency in high-dimensional quantum computations and quantum machine learning. We propose a novel method leveraging entanglement's inherent properties to dynamically modulate probability distributions, offering a potential shift from static approaches. The core mechanism involves introducing controlled entanglement dynamics within a probabilistic framework, leading to improved results and offering new avenues for exploring quantum computation. This work presents a theoretical framework and preliminary experimental investigation, demonstrating the potential of this approach.

Sep 2, 20260 citations

Quantum-Inspired Probability Distributions for Machine Learning

Jincheng Zhang

Quantum-inspired probability distributions are proposed as a novel approach to enhance the robustness and accuracy of machine learning algorithms. This paper explores the potential of leveraging quantum-like phenomena, specifically superposition and entanglement, to mitigate the effects of noise and overfitting, resulting in improved model performance across a range of applications. We detail the core mechanism, outlining the design and implementation of a new distribution family centered around these principles. This work presents a theoretical framework and preliminary experimental results demonstrating the effectiveness of this approach in several machine learning tasks. The core claim is that these distributions offer a significant advancement in robustness compared to traditional probability distributions.

Sep 2, 20260 citations

Quantum Machine Learning Non-linear Learning

Jincheng Zhang

Quantum Machine Learning (QML) represents a paradigm shift in the field of artificial intelligence, leveraging the unique properties of quantum mechanics to develop novel algorithms and computational models. This paper explores the potential of QML to address limitations of classical machine learning, particularly in the realm of non-linear learning and prediction. We propose a new quantum machine learning algorithm centered on the application of quantum entanglement and superposition to enhance data representation and model learning. This approach aims to overcome the challenges associated with linear models in handling complex, non-linear data patterns, thereby offering improved predictive accuracy and generalization capabilities. The core of our investigation involves a detailed analysis of the impact of quantum entanglement and superposition on the training process and resulting model outputs. This research will focus on demonstrating the efficacy of this approach through theoretical analysis and, potentially, preliminary experimental validation. The paper concludes with a discussion of the challenges and future directions of this exciting area of research.

Aug 31, 20260 citations

Quantum Probability Models and Machine Learning: A Collaborative Optimization Approach

Jincheng Zhang

Quantum probability models and machine learning represent a synergistic approach with the potential to significantly enhance the capabilities of both fields. This paper explores a novel method for collaborative optimization, leveraging the unique strengths of quantum computation to accelerate the training and optimization of machine learning models. We present a framework that integrates quantum probability models to enhance feature representation, optimize model parameters, and improve generalization, ultimately leading to improved performance across a range of machine learning tasks. The core mechanism focuses on utilizing quantum algorithms to efficiently explore the parameter space, minimizing computational complexity and enabling the training of models with increased capacity. The approach addresses key challenges in machine learning, particularly in high-dimensional spaces and complex parameter spaces, presenting a promising pathway toward more effective and scalable models.

Sep 5, 20260 citations