Adaptive Quantum State Tomography – Characterizing Complex Quantum States with Efficient Sampling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces Adaptive Quantum State Tomography (AQST), a novel algorithm for efficiently characterizing complex quantum states. Traditional state tomography methods are computationally demanding, particularly for large and highly entangled states. AQST addresses this challenge by dynamically adjusting the sampling strategy during the tomography process, optimizing the reconstruction process and minimizing computational cost. The core mechanism relies on a superposition-based sampling technique, allowing the algorithm to effectively explore the state space and achieve accurate characterization. The paper details the algorithm's design, the rationale behind its key components, and its performance evaluation on benchmark quantum systems. We demonstrate that AQST offers a significant improvement in computational efficiency compared to existing state tomography methods, particularly for complex systems.

DOI: 10.5281/zenodo.22193442

Related papers

Title: Adaptive Quantum State Tomography – Dynamic Reconstruction of Quantum States

Jincheng Zhang

Quantum state tomography is a crucial technique for characterizing complex quantum states, and traditional approaches often struggle with large datasets and computationally intensive reconstruction processes. This paper introduces Adaptive Quantum State Tomography (AQT), a novel algorithm that dynamically adjusts measurement strategies during the reconstruction process to improve accuracy and speed. The core mechanism relies on a learning-based approach, employing a feedback loop to optimize the measurement strategy based on the observed data. This adaptive strategy significantly enhances the reconstruction process, addressing a key bottleneck in quantum state tomography and offering the potential for faster characterization of complex quantum states.

Sep 6, 20260 citations

Adaptive Quantum State Tomography with Reinforcement Learning

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique for characterizing the state of a quantum system. However, traditional QST protocols often require a large number of measurements, leading to significant experimental overhead and uncertainties in the reconstructed state. This paper proposes a novel approach to QST that leverages reinforcement learning (RL) to dynamically adapt measurement strategies. The core idea is to train an RL agent to control the parameters of the QST experiment, learning to optimize the measurement process based on feedback from the acquired data. The agent learns to select measurement bases and angles that minimize the uncertainty in the reconstructed quantum state. This adaptive strategy significantly reduces the number of measurements needed compared to traditional QST while maintaining high accuracy. We present a theoretical framework for this approach, outlining the key components and demonstrating its potential for improved QST performance. The proposed method offers a promising direction for enhancing the efficiency and reliability of QST, contributing to the advancement of quantum information processing.

Sep 7, 20260 citations

Hyperdimensional Computing for Quantum State Tomography

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique for characterizing quantum systems, but it often suffers from significant experimental challenges, including high measurement complexity and resource demands. This paper explores the potential of hyperdimensional computing (HDC) as a viable alternative to traditional QST. HDC leverages high-dimensional vectors to represent and manipulate quantum states, allowing for the development of algorithms that exploit inherent correlations to reconstruct quantum state information with reduced measurement requirements. The core claim is that HDC offers a potentially efficient and scalable approach to QST. This work details the theoretical framework, outlining key algorithms and highlighting the advantages of utilizing HDC for quantum state characterization. The focus is on demonstrating the feasibility of this approach through conceptual design, emphasizing the reduced complexity compared to conventional QST methods. We propose a framework for representing quantum states within HDC vectors and present a simplified reconstruction algorithm.

Aug 28, 20260 citations

Quantum State Tomography for Hardware Verification of Quantum Computers

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique for characterizing quantum states, traditionally employed in research laboratories with highly controlled environments. However, the burgeoning field of quantum computing faces the significant challenge of deploying and verifying quantum hardware in the presence of noise and decoherence, characteristics inherent in current Noisy Intermediate-Scale Quantum (NISQ) devices. This paper proposes an adapted QST algorithm designed to overcome these limitations. The core idea is to systematically reconstruct the density matrix of a qubit or a small quantum system using a series of carefully designed measurements. We address the impact of noise by incorporating error mitigation strategies within the reconstruction process. The algorithm's performance is evaluated through theoretical analysis and conceptual implementation, highlighting its potential to provide a practical and efficient method for hardware verification of NISQ computers. This work establishes a novel approach for characterizing and debugging NISQ hardware, a critical step toward realizing the full potential of quantum computation.

Sep 6, 20260 citations

Quantum State Tomography with Entangled Photon Sources

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique for characterizing quantum states, crucial for validating quantum technologies. However, traditional QST methods often suffer from limitations in measurement efficiency and accuracy, particularly when dealing with complex quantum systems. This work presents an advanced approach to QST utilizing entangled photon sources and a meticulously designed measurement strategy. The core of this method lies in exploiting the correlations inherent in entangled photon pairs to significantly enhance the reconstruction process. We detail a novel scheme that minimizes measurement errors and maximizes the information extracted from each measurement, leading to a substantial improvement in both the accuracy and resolution of the reconstructed quantum state. The theoretical framework outlines the key equations and algorithms used for state reconstruction, focusing on a practical implementation. The results presented suggest a pathway towards more robust and reliable QST, paving the way for more accurate characterization of quantum systems and the development of advanced quantum applications.

Aug 28, 20260 citations

Dynamic Programming for Quantum State Tomography with Entangled Photon Sources

Jincheng Zhang

Quantum state tomography (QST) is a fundamental procedure in quantum information science, enabling the precise characterization of quantum states. Traditional QST methods often require a prohibitive number of measurements, particularly for complex quantum states, significantly limiting their practical application. This work presents a novel approach to QST leveraging dynamic programming. The core idea is to iteratively refine a quantum state estimate based on measurement data, minimizing the total number of measurements needed to achieve a desired level of accuracy. The algorithm constructs a dynamic programming table, storing partial state estimates and their associated probabilities, allowing for efficient exploration of the state space. We demonstrate the efficacy of this approach through theoretical analysis and illustrate its potential advantages over conventional methods. The key contribution lies in the algorithm's ability to adaptively adjust the measurement strategy, focusing on regions of the state space with the highest uncertainty, leading to a substantial reduction in measurement requirements. The performance of the algorithm is discussed in terms of computational complexity and accuracy achieved.

Sep 5, 20260 citations