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Conference on Lasers and Electro-Optics

Weak Measurements Compressed Sensing Quantum State Tomography

Dikla Oren, Yonina C. Eldar, Mordechai Segev

Published Jan 1, 2016• 1 citation
Publisher

Abstract

We show that prior knowledge that a quantum state is close to a pure state enables a direct and efficient measurement of the density matrix representing the state, using the weak measurements methodology.
DOI
10.1364/cleo_qels.2016.fm1n.4

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Recent work has revealed that the wave function of a pure state can be measured directly and that complementary knowledge of a quantum system can be obtained simultaneously by weak measurements. However, the original scheme applies only to pure states, and it is not efficient because most of the data are discarded by post-selection. Here, we propose tomography schemes for pure states and for mixed states via weak measurements, and our schemes are more efficient because we do not discard any data. Furthermore, we demonstrate that any matrix element of a general state can be directly read from an appropriate weak measurement. The density matrix (with all of its elements) represents all that is directly accessible from a general measurement.
We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using O(rdlog²d) measurement settings, compared to standard methods that require d² settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed.
This paper establishes the methods for quantum state tomography based on compressed sensing.These methods are specialized for quantum states that are fairly pure,and they offer a significant performance improvement on large quantum systems.In particular,they are able to reconstruct an unknown density matrix of dimension d and rank r using O(rdlog2d) measurement settings,compared to standard methods that require d2 settings.The methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements,use fast convex optimization,are stable against noise,and can be applied to states that are only approximately low rank.The acquired data can be used to certify that the state is indeed close to pure,so no a priori assumptions are needed.
Quantum state tomography (QST) is a crucial technique for characterizing quantum states, forming the bedrock of quantum information processing and quantum metrology. However, traditional QST relies on a large number of measurements, making it computationally expensive and experimentally demanding. This work addresses this challenge by proposing a novel algorithm that integrates compressed sensing (CS) and Bayesian inference to achieve accurate quantum state reconstruction from limited measurement data. The core idea is to exploit the inherent sparsity of quantum states and leverage CS principles to significantly reduce the required measurement count. The algorithm utilizes a sparse dictionary learned from prior knowledge of the quantum state and employs Bayesian inference to estimate the state parameters, providing a robust and efficient reconstruction process. The results demonstrate the feasibility and effectiveness of this approach, offering a pathway towards more practical and accessible QST methods. Key mathematical formulations are presented, including the Bayesian inference framework and the CS reconstruction process.
Quantum tomography is the most informative tool for estimating the quality of preparation and transformation of quantum states. Its development is crucially necessary for debugging of developed quantum processors. Many existing methods of quantum tomography differ in types of performed measurements and in procedures of their processing. The practical implementation of quantum tomography requires the comparison of different methods, which is complicated because of the absence of a general methodology of estimation. A universal methodology based on numerical experiments has been proposed in this work to estimate the quality of quantum state tomography methods. The developed methodology has been applied to three quantum tomography methods (root approach, compressed sensing, and adaptive tomography) efficiently operating with almost pure states, which is relevant for the current technological foundation of the experiments.
Efficient acquiring information from a quantum state is important for research in fundamental quantum physics and quantum information applications. Instead of using standard quantum state tomography method with reconstruction algorithm, weak values were proposed to directly measure density matrix elements of quantum state. Recently, similar to the concept of weak value, modular values were introduced to extend the direct measurement scheme to nonlocal quantum wavefunction. However, this method still involves approximations, which leads to inherent low precision. Here, we propose a new scheme which enables direct measurement for ideal value of the nonlocal density matrix element without taking approximations. Our scheme allows more accurate characterization of nonlocal quantum states, and therefore has greater advantages in practical measurement scenarios.