Quantum-Enhanced Uncertainty Quantification for Complex Systems

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-enhanced uncertainty quantification (QEQ) offers a paradigm shift in quantifying uncertainty in complex systems, surpassing classical methods' limitations. This paper explores the application of quantum computing to this field, focusing on the development of a novel algorithm for probabilistic distribution estimation. We argue that quantum algorithms can provide a fundamentally more accurate and robust representation of system behavior, leading to improved risk assessment and decision-making in critical applications. The core mechanism centers around leveraging quantum phenomena to enhance the computational complexity of probability distribution estimation, resulting in significantly improved precision and speed. This work presents a preliminary framework demonstrating the potential of quantum computing to revolutionize uncertainty quantification.

DOI: 10.5281/zenodo.22330700

Related papers

Quantum-Inspired Probabilistic Programming for Uncertainty Quantification

Jincheng Zhang

This paper introduces a novel approach to uncertainty quantification (UQ) by drawing inspiration from quantum mechanics. Traditional UQ methods, particularly Monte Carlo simulations, often struggle with the curse of dimensionality, exhibiting diminishing returns as the number of variables increases. We propose a framework utilizing simulated quantum measurement processes to efficiently explore probability spaces, aiming for significant improvements in both speed and accuracy. The core concept involves translating probabilistic programming logic into a sequence of simulated quantum measurements, mimicking the principles of superposition and entanglement. This approach moves beyond the limitations of classical sampling techniques and potentially unlocks exponential speedups in complex system modeling. The presented methodology focuses on a new programming language and runtime designed specifically for this quantum-inspired probabilistic programming paradigm. We explore the theoretical underpinnings and outline the key components of this system, highlighting the advantages it offers over conventional UQ methods. The design prioritizes computational efficiency and scalability, crucial aspects for tackling real-world problems characterized by high-dimensional uncertainty. The ultimate goal is to develop a robust and practical tool for accurately assessing risk and making informed decisions in complex, uncertain environments.

Aug 28, 20260 citations

Quantum-Enhanced Probability Distribution Algorithms

Jincheng Zhang

Quantum-enhanced probability distribution algorithms represent a significant advancement in computational modeling, particularly for complex probabilistic scenarios. This paper explores the potential of leveraging quantum mechanics to dramatically improve the accuracy and speed of Monte Carlo simulations, addressing the inherent limitations of classical approaches. The core claim is to develop a novel algorithm that utilizes quantum superposition and entanglement to efficiently explore the space of possible distributions, thereby reducing computational cost and enhancing performance. The proposed mechanism centers around exploiting quantum phenomena to overcome the computational bottlenecks encountered in traditional Monte Carlo methods, offering a paradigm shift in probabilistic modeling. This work will detail the theoretical foundations of the proposed algorithm, its implementation and testing with illustrative examples, and its potential impact on various applications.

Aug 29, 20260 citations

Quantum-Enhanced Bayesian Inference for Uncertainty Quantification

Jincheng Zhang

Quantum-enhanced Bayesian inference offers a promising approach to address the computational challenges associated with uncertainty quantification, particularly in situations demanding high accuracy and speed. This paper explores the design and implementation of a quantum algorithm, quantum state tomography, to enhance Bayesian inference, aiming to mitigate the inherent cost of traditional methods. We detail the theoretical framework underpinning this approach, highlighting its potential to significantly improve reliability and speed. The core claim centers on leveraging quantum state tomography to achieve a more precise estimate of the system state, thereby bolstering the accuracy of Bayesian inference. This work represents a novel investigation into the application of quantum mechanics to Bayesian inference, with the potential to deliver substantial improvements in computational efficiency and robustness.

Sep 1, 20260 citations

Quantum-Enhanced Probability Density Estimation for Complex Systems

Jincheng Zhang

This paper investigates the application of quantum entanglement to improve the estimation of probability density within complex systems. The core claim posits the design of a quantum algorithm, termed "Quantum Entangled Probability," that leverages entanglement to accelerate and enhance predictive modeling capabilities. Current probabilistic methods face significant computational challenges, particularly when dealing with intricate systems exhibiting high dimensionality and complex dependencies. Quantum computing, through its unique properties of superposition and entanglement, offers the potential to overcome these limitations, providing a pathway to more accurate and efficient predictions. This research explores the theoretical foundations and practical implementation of this novel approach, focusing on its potential impact on diverse fields requiring precise probabilistic modeling. The paper details the Quantum Entangled Probability algorithm, its underlying principles, and preliminary experimental results demonstrating its efficacy in a simulated environment.

Sep 4, 20260 citations

Quantum-Enhanced Bayesian Inference for Predictive Modeling

Jincheng Zhang

Quantum-enhanced Bayesian inference offers a promising paradigm shift in predictive modeling, potentially surpassing the limitations of classical methods. This paper explores the application of quantum superposition to enhance Bayesian inference, specifically focusing on improving the accuracy of complex systems. We outline a novel algorithm that utilizes quantum superposition to represent probability distributions, enabling efficient exploration of parameter spaces and ultimately leading to more accurate predictions. The core mechanism centers around exploiting the unique properties of quantum systems to accelerate the Bayesian inference process. The research presents a theoretical framework and preliminary implementation demonstrating the potential of this approach.

Sep 4, 20260 citations

Quantum-Enhanced Bayesian Network Propagation for Risk Assessment

Jincheng Zhang

This paper investigates the potential of quantum computing to enhance Bayesian Network (BN) propagation for risk assessment. Traditional BN models struggle with scalability and computational complexity, particularly when dealing with long-time horizons and complex scenarios. We propose a novel framework that leverages quantum algorithms to accelerate probabilistic model updates, thereby improving accuracy and enabling more sophisticated risk assessment. This work explores the feasibility of quantum-enhanced BN propagation through the application of variational quantum eigensolver (VQE) and quantum annealing techniques. The core claim is that quantum computing offers a significant advantage in risk assessment by improving model accuracy and providing a more robust representation of uncertainty.

Aug 31, 20260 citations