Quantum Phase Estimation: A Novel Adaptive Optimization Algorithm for Quantum State Estimation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum phase estimation (QPE) is a fundamental technique for determining the phase of a quantum state, crucial for various quantum algorithms and quantum measurements. This paper introduces a novel adaptive optimization algorithm, dubbed the "Adaptive Quantum Phase Estimation" (AQPE) algorithm, designed to enhance the efficiency of QPE by dynamically adjusting parameters during the state estimation process. We demonstrate the effectiveness of AQPE through rigorous theoretical analysis and extensive numerical simulations, achieving significantly improved convergence rates compared to traditional optimization techniques. The algorithm leverages a feedback mechanism based on the observed phase error during the estimation, allowing it to iteratively refine the parameter set and ultimately locate the optimal phase estimation. We present preliminary results demonstrating the algorithm's capability to mitigate the challenges associated with conventional QPE, particularly in the presence of noisy data. The implications of this research extend to broader advancements in quantum algorithm design and computational power.

DOI: 10.5281/zenodo.22197995

Related papers

Core Claim: Design a self-adaptive quantum error correction algorithm that dynamically adjusts quantum bit phases based on quantum error correction failure patterns, thereby enhancing error correction efficiency.

Jincheng Zhang

Quantum error correction (QEC) is a crucial component of quantum computing, enabling the reliable transmission and manipulation of quantum information. Traditional QEC schemes often rely on fixed phase adjustments, which can be suboptimal in certain scenarios. This paper introduces a novel self-adaptive quantum error correction algorithm, termed "Quantum Phase Evolution," that dynamically adjusts quantum bit phases based on the observed error patterns. Our approach leverages a 'Quantum Phase Evolution' mechanism to optimize the error correction process by tailoring the qubit states in response to feedback loops generated during error correction. We demonstrate the effectiveness of this algorithm through simulations and preliminary experiments, achieving significant improvements in error correction fidelity compared to conventional methods. The core mechanism is to iteratively refine the quantum state based on the error patterns, offering a more adaptive and potentially more efficient approach to QEC.

Sep 4, 20260 citations

Dynamic Quantum State Reconstruction: An Adaptive Optimization for Quantum Computing Efficiency

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and efficient quantum algorithms are crucial for realizing this potential. However, the inherent complexity of quantum systems often necessitates intricate parameter adjustments, which can limit scalability and algorithm performance. This paper introduces a novel dynamic quantum state reconstruction algorithm, designed to optimize quantum computations through adaptive parameter adjustments. We propose a method that leverages the quantum state's inherent self-consistency, enabling efficient and precise recalibration of quantum states. This approach directly addresses the limitations of traditional parameter tuning methods, offering a pathway towards significant improvements in quantum algorithm efficiency and overall system performance. The algorithm's core mechanism centers around a feedback loop that continuously adjusts quantum state parameters based on observed results, ensuring optimal resource utilization. The study demonstrates the effectiveness of this algorithm through simulations and preliminary experimental results, highlighting its potential to enhance quantum computation capabilities.

Sep 4, 20260 citations

Quantum Algorithms' Adaptive Optimization and Expansion

Jincheng Zhang

Quantum algorithms hold immense potential for revolutionizing various fields, and a key challenge in their development lies in optimizing their performance across diverse input datasets. This paper proposes a novel approach to quantum algorithm optimization centered around a dynamically adaptive optimization framework. We introduce a quantum algorithm that adapts its parameter adjustments based on the characteristics of the input data, achieving improved efficiency and performance through real-time parameter tuning. This system, termed "Quantum Adaptive Tuning," leverages a combination of quantum optimization techniques with a dynamic parameter adjustment mechanism. The core mechanism is to iteratively refine the quantum algorithm's parameters to maximize the likelihood of finding optimal solutions. We present a theoretical analysis and initial experimental validation demonstrating the effectiveness of this approach. The paper concludes with a discussion of the implications of this work and future research directions.

Sep 4, 20260 citations

Adaptive Quantum Computing Algorithm for Quantum State Generation

Jincheng Zhang

Quantum computing holds immense potential for scientific discovery and technological advancement, but the precise control and manipulation of quantum states remain a significant challenge. This paper introduces an adaptive quantum state generation algorithm designed to address this challenge by dynamically adjusting quantum state generation parameters. The algorithm leverages a self-tuning mechanism that continuously optimizes the state generation process based on real-time feedback. This approach offers improved control, reduced error rates, and enhanced optimization capabilities compared to traditional methods. The core of this algorithm involves a feedback loop that analyzes and modifies the parameters affecting the quantum state, ultimately leading to more accurate and reliable quantum state generation. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of this adaptive approach. This work represents a significant step towards automated and robust quantum state generation, paving the way for more complex quantum computations.

Aug 31, 20260 citations

Dynamic Quantum Adaptive Algorithms

Jincheng Zhang

This paper introduces a novel dynamic quantum adaptive algorithm designed to optimize quantum computation by dynamically adjusting quantum system parameters in response to input data. The core mechanism relies on a quantum self-adaptation process, allowing the system to evolve and refine its state based on the characteristics of the data being processed. We present a detailed theoretical analysis and preliminary implementation demonstrating the algorithm's effectiveness in adapting to varying input sequences, resulting in improved performance compared to static quantum algorithms in specific scenarios. The algorithm's success lies in its ability to continuously refine the quantum state, mitigating the limitations of traditional approaches.

Sep 6, 20260 citations

Quantum Phase Field Simulation: Adaptive Optimization for Enhanced Simulation

Jincheng Zhang

Quantum phase field (QPF) simulations offer a powerful tool for studying complex quantum systems, particularly in areas such as condensed matter physics and quantum materials. However, traditional QPF simulations often require extensive parameter tuning, which can be time-consuming and limit the exploration of the parameter space. This paper introduces a novel adaptive optimization algorithm for QPF simulations, designed to automatically adjust simulation parameters based on the resulting data, thereby significantly improving simulation accuracy and efficiency. The algorithm leverages the inherent dynamics of quantum phase field simulations to dynamically adjust parameters, ensuring a robust and efficient simulation process. We demonstrate the effectiveness of our approach through a series of simulations, showcasing its ability to achieve superior results compared to existing optimization techniques.

Sep 2, 20260 citations