Title: Quantum State Evolution via Adaptive Symmetry Breaking

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum state evolution, a cornerstone of quantum computation, is inherently susceptible to decoherence due to environmental interactions. Traditional algorithms often rely on fixed parameters, failing to effectively address this challenge. This paper proposes a novel quantum state evolution algorithm, Quantum State Evolution via Adaptive Symmetry Breaking (QSEAB), that dynamically adjusts the symmetry breaking rate to mitigate decoherence. We demonstrate the effectiveness of QSEAB through extensive simulations and analysis, showcasing improved stability and fidelity compared to existing methods. The core mechanism involves a feedback loop that continuously monitors the system's evolution and optimizes the symmetry breaking process to minimize the impact of noise. This results in a more robust and predictable quantum state evolution, offering a significant advancement in the field.

DOI: 10.5281/zenodo.22192146

Related papers

Title: Adaptive Quantum Algorithm Optimization of Quantum Systems

Jincheng Zhang

This paper presents a novel quantum system optimization algorithm based on adaptive quantum system dynamics. Traditional optimization methods often rely on fixed parameters, limiting the potential for improved performance. We propose a dynamically adjustable algorithm that leverages the inherent properties of quantum systems to optimize them in real-time. The core mechanism involves a continuous feedback loop that adjusts algorithm parameters based on the quantum system's state, resulting in enhanced efficiency compared to static optimization techniques. This approach addresses the limitations of current algorithms by providing a framework for adapting to the specific characteristics of each quantum system. The design incorporates a novel weighting scheme to determine the impact of each parameter adjustment, ensuring a balanced and effective optimization process. The results demonstrate significant improvements in various quantum system optimization tasks, particularly in scenarios with complex dynamics.

Sep 3, 20260 citations

Title: Adaptive Quantum Simulation of Dynamical Systems

Jincheng Zhang

Quantum simulation offers a powerful tool for studying complex systems, but the inherent complexity of these systems often necessitates meticulously tuned parameters. Traditional methods frequently rely on fixed parameter settings, limiting the system's adaptability. This paper proposes a novel adaptive quantum simulation technique that dynamically adjusts simulation parameters based on the observed dynamics of a dynamical system. We introduce a feedback loop that continuously optimizes these parameters to maintain system stability and fidelity. This represents a significant advancement in quantum simulation, enabling a more flexible and robust approach to modeling dynamic systems. This work aims to address the limitations of current quantum simulation techniques by providing a system capable of self-optimizing parameters, thus facilitating the study of systems with varying degrees of complexity. The core mechanism involves a continuous feedback loop that adjusts simulation parameters based on observed dynamics, fostering a dynamic and adaptive simulation process.

Sep 4, 20260 citations

Title: Adaptive Quantum Algorithm Parameter Adjustment

Jincheng Zhang

Quantum algorithms, renowned for their potential to solve complex problems beyond classical computation, are notoriously sensitive to initial parameters. Traditional parameter adjustment methods often rely on manual tuning, which can be time-consuming and limited in their ability to adapt to the algorithm's unique characteristics. This paper presents a novel approach to quantum algorithm parameter adjustment utilizing an adaptive mechanism based on the inherent quantum state self-similarity. We propose a system that continuously monitors and adjusts key parameters within a quantum circuit, leveraging the quantum state's dynamic nature to optimize performance and stability. The core mechanism centers around a feedback loop that dynamically adjusts parameters based on the observed evolution of the quantum state. This adaptation allows for more robust and efficient parameter tuning, potentially unlocking significant improvements in algorithm execution speed and accuracy. We investigate the effects of this adaptive approach through theoretical analysis and preliminary experimental validation.

Aug 29, 20260 citations

Title: Adaptive Phase-Coherence Synchronization for Quantum Algorithms

Jincheng Zhang

Quantum algorithms rely heavily on precise phase-coherence, a delicate phenomenon crucial for computation. Current synchronization protocols often employ fixed parameters, limiting the algorithm's performance. This paper investigates and presents an adaptive phase-coherence synchronization protocol, designed to dynamically adjust synchronization parameters based on the evolving phase-coherence dynamics of quantum systems. We propose a novel approach that leverages a feedback mechanism to optimize synchronization fidelity, addressing limitations of existing methods and opening avenues for enhanced quantum algorithm execution. The core mechanism involves a self-regulating algorithm that continually monitors the quantum state and adjusts synchronization parameters to maintain optimal phase-coherence. This adaptability will significantly improve the fidelity of quantum algorithms, offering a more robust and efficient solution.

Sep 7, 20260 citations

Title: Adaptive Quantum Feedback Loops for Algorithmic Stability

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing numerous fields, yet the inherent instability of quantum algorithms poses a significant challenge. This paper investigates the application of adaptive quantum feedback loops to enhance algorithmic stability, addressing the problem of divergence and ensuring consistent performance across a wide range of conditions. We introduce a novel system of continuous monitoring and adjustment, leveraging the inherent dynamics of quantum systems to proactively mitigate instability. The core mechanism centers around a feedback loop that dynamically adjusts quantum parameters based on the algorithm's output, effectively correcting for deviations and promoting stability. This approach offers a potential pathway to significantly improve the reliability and predictability of quantum computations.

Aug 31, 20260 citations

Title: Adaptive Parameter Adjustment for Dynamic Quantum Algorithms

Jincheng Zhang

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.

Sep 3, 20260 citations