Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Quantum Information

Online quantum state tomography of N-qubit via continuous weak measurement and compressed sensing

Sajede Harraz, Shuang Cong, Kezhi Li

Published Oct 30, 2020• 2 citations
Publisher

Abstract

In this paper, we propose an online state tomography method for [Formula: see text]-qubit quantum system based on the continuous weak measurement and compressed sensing (CS). The quantum system is described by stochastic master equation. The continuous weak measurement operators for the [Formula: see text]-qubit quantum system, which are indirectly acted on the quantum system, are derived according to the measurement operator results of two-level quantum system. The online time-varying measurement operators are obtained by means of the dynamic evolution equation of the system. The quantum state is online estimated by solving the optimization problem of minimizing the two-norm with the positive definite constraints of density matrix, and we use the nonnegative least squares algorithm to solve the optimization problem. CS theory is used to reduce the number of the measurements in the process of online state estimation. In the numerical experiments, we study the effects of external control field, measurement rate and the different numbers of qubits on the performance in the proposed method. The minimum required numbers of measurements for 2, 3, 4 and 5 qubits are found. The normalized distance and fidelity of our proposed method can achieve satisfying accuracy with small number of measurements.

DOI
10.1142/s0219749920400067

Related papers

On-Line State Estimation of 2-qubit Quantum Systems

Yaru Tang, Shuang Cong

In this paper, the online estimation of 2-qubit states is studied based on the quantum continuous weak measurement and compressed sensing (CS) theory. The stochastic master equation is used to describe the dynamic evolution of the system. The weak measurement operator acting on the system and the dynamic equation of the measurement operator over time are derived. The number of measurements is reduced based on the CS theory. The non-negative LS algorithm is used to reconstruct the quantum system state in free evolution. At last, the numerical simulations of the on-line state estimation of 2-qubit quantum systems are carried out in MATLAB environment, and the influences of the parameters on the estimation results are analyzed by the performance comparisons.

Mediterranean Conference on Control and Automation (MED)•Jul 1, 2019•0 citations

State Transfer of Two-level Quantum System Feedback Control Based on Online State Estimation

Shuang Cong, Yaru Tang, Kezhi Li

A quantum state feedback control method is proposed in this paper. The state of a two-level open quantum system is estimated online based on the continuous weak measurement and the compressed sensing theory. Based on the state estimated online and the Lyapunov stability theorem, the state feedback control law used to transfer the quantum state is designed. Moreover, three numerical simulation experiments are implemented in the MATLAB environment: the state transfer from eigenstates to eigenstates, superposition states to superposition states, and superposition states to mixed states. The experimental results verify high performance of the proposed feedback control based on the state estimated online.

Chinese Control Conference (CCC)•Jul 26, 2021•2 citations

A reconstruction algorithm for compressive quantum tomography using various measurement sets

Zheng Kai, Kezhi Li, Shuang Cong

Abstract Compressed sensing (CS) has been verified that it offers a significant performance improvement for large quantum systems comparing with the conventional quantum tomography approaches, because it reduces the number of measurements from O ( d 2 ) to O ( rd log( d )) in particular for quantum states that are fairly pure. Yet few algorithms have been proposed for quantum state tomography using CS specifically, let alone basis analysis for various measurement sets in quantum CS. To fill this gap, in this paper an efficient and robust state reconstruction algorithm based on compressive sensing is developed. By leveraging the fixed point equation approach to avoid the matrix inverse operation, we propose a fixed-point alternating direction method algorithm for compressive quantum state estimation that can handle both normal errors and large outliers in the optimization process. In addition, properties of five practical measurement bases (including the Pauli basis) are analyzed in terms of their coherences and reconstruction performances, which provides theoretical instructions for the selection of measurement settings in the quantum state estimation. The numerical experiments show that the proposed algorithm has much less calculating time, higher reconstruction accuracy and is more robust to outlier noises than many existing state reconstruction algorithms.

Scientific Reports•Dec 14, 2016•26 citations

Compressed Sensing Quantum State Tomography Assisted by Adaptive Design

Qi Yin, Guo‐Yong Xiang, Chuan‐Feng Li +1 more

The recently proposed compressed sensing (CS) method sheds light on quantum state tomography of multi-qubit systems with low rank, which greatly reduces the complexity of measurement and computation. However, the restricted isometry property requirement of CS is difficult to be promised or verified in practice, which makes this method probably assign unreasonable results. In regard to this problem, we adopt a two-step procedure and implement an adaptive strategy to update measurement operators based on the measurement results of the first step for CS, which not only serves as a way to verify the estimate but also improves the accuracy of tomography. Our numerical simulations manifest that our adaptive protocol can reduce about half of the infidelity of non-adaptive protocol and is still efficient even when the rank of the state is slightly high, which would greatly benefit multi-qubit state tomography in future experiments .

Chinese Physics Letters•Jul 1, 2018•3 citations

Quantum state tomography by continuous measurement and compressed sensing

Aaron C.T. Smith, Carlos A. Riofrío, Brian E. Anderson +3 more

The need to perform quantum state tomography on ever-larger systems has spurred a search for methods that yield good estimates from incomplete data. We study the performance of compressed sensing (CS) and least squares (LS) estimators in a fast protocol based on continuous measurement on an ensemble of cesium atomic spins. They both efficiently reconstruct nearly pure states in the 16-dimensional ground manifold, reaching average fidelities ${\overline{\mathcal{F}}}_{\mathrm{CS}}=0.92$ and ${\overline{\mathcal{F}}}_{\mathrm{LS}}=0.88$ using similar amounts of incomplete data. Surprisingly, the main advantage of CS in our protocol is an increased robustness to experimental imperfections.

arXiv:1208.5015•Physical Review A•Mar 11, 2013•65 citations

Quantum State Tomography via Compressed Sensing

Jincheng Zhang

Quantum state tomography (QST) is a crucial technique for characterizing the state of quantum systems. However, traditional QST requires a large number of measurements, leading to significant experimental overhead in terms of time, resources, and data acquisition. This work proposes a novel approach to QST by leveraging compressed sensing (CS) techniques. CS allows for accurate reconstruction of the quantum state from a significantly reduced set of measurements, thereby mitigating the experimental burden. We employ sparsity regularization, a key component of CS, to minimize the number of measurements needed. The theoretical framework and the underlying principles are presented, focusing on the mathematical formulation of the problem and the optimization strategies involved. This method offers a pathway to more efficient and cost-effective QST, particularly for complex quantum states or when limited experimental resources are available. We demonstrate the potential of this approach through a detailed mathematical analysis.

•Sep 5, 2026•0 citations
In this paper, we propose an online state tomography method for [Formula: see text]-qubit quantum system based on the continuous weak measurement and compressed sensing (CS). The quantum system is described by stochastic master equation. The continuous weak measurement operators for the [Formula: see text]-qubit quantum system, which are indirectly acted on the quantum system, are derived according to the measurement operator results of two-level quantum system. The online time-varying measurement operators are obtained by means of the dynamic evolution equation of the system. The quantum state is online estimated by solving the optimization problem of minimizing the two-norm with the positive definite constraints of density matrix, and we use the nonnegative least squares algorithm to solve the optimization problem. CS theory is used to reduce the number of the measurements in the process of online state estimation. In the numerical experiments, we study the effects of external control field, measurement rate and the different numbers of qubits on the performance in the proposed method. The minimum required numbers of measurements for 2, 3, 4 and 5 qubits are found. The normalized distance and fidelity of our proposed method can achieve satisfying accuracy with small number of measurements.
In this paper, the online estimation of 2-qubit states is studied based on the quantum continuous weak measurement and compressed sensing (CS) theory. The stochastic master equation is used to describe the dynamic evolution of the system. The weak measurement operator acting on the system and the dynamic equation of the measurement operator over time are derived. The number of measurements is reduced based on the CS theory. The non-negative LS algorithm is used to reconstruct the quantum system state in free evolution. At last, the numerical simulations of the on-line state estimation of 2-qubit quantum systems are carried out in MATLAB environment, and the influences of the parameters on the estimation results are analyzed by the performance comparisons.
A quantum state feedback control method is proposed in this paper. The state of a two-level open quantum system is estimated online based on the continuous weak measurement and the compressed sensing theory. Based on the state estimated online and the Lyapunov stability theorem, the state feedback control law used to transfer the quantum state is designed. Moreover, three numerical simulation experiments are implemented in the MATLAB environment: the state transfer from eigenstates to eigenstates, superposition states to superposition states, and superposition states to mixed states. The experimental results verify high performance of the proposed feedback control based on the state estimated online.
Abstract Compressed sensing (CS) has been verified that it offers a significant performance improvement for large quantum systems comparing with the conventional quantum tomography approaches, because it reduces the number of measurements from O ( d 2 ) to O ( rd log( d )) in particular for quantum states that are fairly pure. Yet few algorithms have been proposed for quantum state tomography using CS specifically, let alone basis analysis for various measurement sets in quantum CS. To fill this gap, in this paper an efficient and robust state reconstruction algorithm based on compressive sensing is developed. By leveraging the fixed point equation approach to avoid the matrix inverse operation, we propose a fixed-point alternating direction method algorithm for compressive quantum state estimation that can handle both normal errors and large outliers in the optimization process. In addition, properties of five practical measurement bases (including the Pauli basis) are analyzed in terms of their coherences and reconstruction performances, which provides theoretical instructions for the selection of measurement settings in the quantum state estimation. The numerical experiments show that the proposed algorithm has much less calculating time, higher reconstruction accuracy and is more robust to outlier noises than many existing state reconstruction algorithms.
The recently proposed compressed sensing (CS) method sheds light on quantum state tomography of multi-qubit systems with low rank, which greatly reduces the complexity of measurement and computation. However, the restricted isometry property requirement of CS is difficult to be promised or verified in practice, which makes this method probably assign unreasonable results. In regard to this problem, we adopt a two-step procedure and implement an adaptive strategy to update measurement operators based on the measurement results of the first step for CS, which not only serves as a way to verify the estimate but also improves the accuracy of tomography. Our numerical simulations manifest that our adaptive protocol can reduce about half of the infidelity of non-adaptive protocol and is still efficient even when the rank of the state is slightly high, which would greatly benefit multi-qubit state tomography in future experiments .
The need to perform quantum state tomography on ever-larger systems has spurred a search for methods that yield good estimates from incomplete data. We study the performance of compressed sensing (CS) and least squares (LS) estimators in a fast protocol based on continuous measurement on an ensemble of cesium atomic spins. They both efficiently reconstruct nearly pure states in the 16-dimensional ground manifold, reaching average fidelities F‾CS=0.92{\overline{\mathcal{F}}}_{\mathrm{CS}}=0.92FCS​=0.92 and F‾LS=0.88{\overline{\mathcal{F}}}_{\mathrm{LS}}=0.88FLS​=0.88 using similar amounts of incomplete data. Surprisingly, the main advantage of CS in our protocol is an increased robustness to experimental imperfections.
Quantum state tomography (QST) is a crucial technique for characterizing the state of quantum systems. However, traditional QST requires a large number of measurements, leading to significant experimental overhead in terms of time, resources, and data acquisition. This work proposes a novel approach to QST by leveraging compressed sensing (CS) techniques. CS allows for accurate reconstruction of the quantum state from a significantly reduced set of measurements, thereby mitigating the experimental burden. We employ sparsity regularization, a key component of CS, to minimize the number of measurements needed. The theoretical framework and the underlying principles are presented, focusing on the mathematical formulation of the problem and the optimization strategies involved. This method offers a pathway to more efficient and cost-effective QST, particularly for complex quantum states or when limited experimental resources are available. We demonstrate the potential of this approach through a detailed mathematical analysis.