Adaptive Quantum State Tomography with Reinforcement Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22632831

Related papers

Adaptive Quantum Control via Reinforcement Learning

Jincheng Zhang

This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel framework where an RL agent learns to dynamically adjust quantum control pulses in real-time, maximizing the fidelity of quantum operations. The agent receives feedback from the quantum system, typically measured through state variables, and utilizes this information to optimize the control pulse sequence. This adaptive approach addresses the limitations of static control schemes and promises enhanced robustness and performance in complex quantum systems. The core claim centers on utilizing reinforcement learning to design optimal quantum control pulses. The central mechanism involves a trained controller dynamically adjusting pulses based on system feedback. The presented methodology offers a new approach to quantum control, particularly relevant in noisy environments and systems with parameter variations. The research demonstrates the potential of RL for achieving superior quantum control outcomes compared to conventional techniques. ---

Sep 4, 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

Adaptive Quantum State Measurement and Information Extraction

Jincheng Zhang

This paper investigates the development of an adaptive quantum state measurement protocol designed to enhance information extraction efficiency from quantum states while mitigating the detrimental effects of decoherence. The core idea revolves around employing a reinforcement learning algorithm to dynamically adjust the measurement basis, based on the observed quantum state characteristics and feedback signals generated during the measurement process. Real-time adjustments to measurement device parameters via quantum control techniques further optimize the measurement outcome. The proposed method addresses the limitations of traditional fixed-basis measurements, which are highly susceptible to decoherence. Through adaptive basis selection, this approach aims to maximize information extraction and minimize interference with the quantum state. The theoretical framework and underlying principles are presented, outlining the key components and their interrelationship. The potential impact of this technique on quantum information processing is discussed.

Aug 29, 20260 citations

Quantum State Tomography via Variational Quantum Algorithms

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique in quantum information science, aiming to reconstruct the density matrix of an unknown quantum state from experimental measurements. However, traditional QST methods often suffer from significant computational demands and are highly susceptible to measurement noise, hindering their practical application, particularly with near-term quantum computers. This work proposes a novel approach to QST utilizing variational quantum algorithms (VQAs). VQAs iteratively optimize a parameterized quantum circuit to minimize a cost function related to the fidelity between the desired state and the state predicted by the circuit. This approach dramatically reduces the number of required measurements and offers the potential for achieving high-fidelity state reconstruction with lower quantum resource requirements. We detail the theoretical framework, outlining the core algorithms and mathematical formulations. The key advantage lies in the ability to learn the state directly, mitigating the limitations of traditional methods. We explore the optimization landscape and discuss strategies for improving convergence and robustness. The results demonstrate a promising path toward scalable and robust QST on quantum devices.

Aug 29, 20260 citations

Fractal Information Theory for Quantum State Tomography

Jincheng Zhang

Quantum state tomography (QST) is a fundamental technique for characterizing the state of a quantum system. However, traditional QST protocols often suffer from significant measurement complexity and resource demands, particularly when dealing with high-dimensional quantum states. This work proposes a novel approach to QST that leverages the inherent fractal structure observed in measurement outcomes. We introduce a framework utilizing fractal dimension analysis to guide the acquisition of more efficient and accurate tomography protocols. The core idea is to adaptively refine measurement strategies based on the fractal characteristics revealed in the measurement data. This approach aims to reduce the overall number of measurements required while simultaneously improving the fidelity of the reconstructed quantum state. We demonstrate the theoretical feasibility and potential benefits of this methodology, offering a promising direction for advancing QST techniques.

Aug 31, 20260 citations

Adaptive Quantum Control Algorithm Optimization

Jincheng Zhang

This paper investigates the optimization of quantum control algorithms using reinforcement learning (RL). The core claim is that RL can significantly enhance the control precision and efficiency of quantum devices. We propose a framework where the quantum control algorithm is treated as an intelligent agent, interacting with the quantum environment to learn optimal control strategies. This approach allows for adaptive control, addressing the challenges inherent in designing fixed control protocols for complex quantum systems. The methodology involves defining a reward function that reflects the desired quantum state evolution, using RL algorithms to train the agent, and validating the learned control strategy through simulations. We demonstrate the potential of this approach to improve control accuracy and robustness, particularly in scenarios with noisy environments or rapidly changing system parameters. The results suggest a promising direction for leveraging machine learning techniques to unlock the full potential of quantum control.

Sep 6, 20260 citations