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Characterizing complex quantum systems is a vital task in quantum information science. Quantum tomography, the standard tool used for this purpose, uses a well-designed measurement record to reconstruct quantum states and processes. It is,…

Quantum Physics · Physics 2015-12-10 Amir Kalev , Robert L. Kosut , Ivan H. Deutsch

In this paper, we present an efficient weak measurement-based scheme for direct quantum state tomography (DQST) and direct quantum process tomography (DQPT), and experimentally implement it on an NMR ensemble quantum information processor…

Quantum Physics · Physics 2024-11-05 Akshay Gaikwad , Gayatri Singh , Kavita Dorai , Arvind

We consider realistic measurement systems, where measurements are accompanied by decoherence processes. The aim of this work is the construction of methods and algorithms for precise quantum measurements with fidelity close to the…

Quantum Physics · Physics 2017-01-10 Yu. I. Bogdanov , B. I. Bantysh , N. A. Bogdanova , A. B. Kvasnyy , V. F. Lukichev

Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies: The effort of quantum tomography---the characterization of processes and states within a quantum…

Quantum Physics · Physics 2017-05-26 C. A. Riofrio , D. Gross , S. T. Flammia , T. Monz , D. Nigg , R. Blatt , J. Eisert

We propose a high efficiency tomographic scheme to reconstruct an unknown quantum state of the qubits by using a series of quantum nondemolition (QND) measurements. The proposed QND measurements of the qubits are implemented by probing the…

Quantum Physics · Physics 2015-05-20 J. S. Huang , L. F. Wei , C. H. Oh

Quantum state tomography (QST) is one of the fundamental problems in quantum information. Among various metrics, sample complexity is widely used to evaluate QST algorithms. While multi-copy measurements are known to achieve optimal sample…

Quantum Physics · Physics 2025-09-17 Gyungmin Cho , Dohun Kim

Bottom-up coarse-grained (CG) modeling expands the spatial and temporal scales of molecular simulation by seeking a reduced, thermodynamically consistent representation of an atomistic model. Developments in CG theory have largely focused…

Chemical Physics · Physics 2025-03-28 Patrick G. Sahrmann , Gregory A. Voth

We present a supervised machine learning-based method using convolutional neural networks to estimate the covariance matrix of Gaussian quantum states in the presence of thermal noise. Unlike computationally intensive density matrix…

We present a framework that formulates the quest for the most efficient quantum state tomography scheme as an optimization problem which can be solved numerically. This approach can be applied to a broad spectrum of relevant setups…

Quantum Physics · Physics 2021-12-17 Violeta N. Ivanova-Rohling , Guido Burkard , Niklas Rohling

It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension independent of the number of qubits. We show that full quantum…

Quantum Physics · Physics 2022-07-14 Alexander Lidiak , Casey Jameson , Zhen Qin , Gongguo Tang , Michael B. Wakin , Zhihui Zhu , Zhexuan Gong

In this article we propose a new approach to quantum measurement in reference to the stroboscopic tomography. Generally, in the stroboscopic approach it is assumed that the information about the quantum system is encoded in the mean values…

Quantum Physics · Physics 2020-07-30 Artur Czerwiński

Quantum state tomography, which aims to find the best description of a quantum state -- the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of…

Quantum Physics · Physics 2022-11-18 Markus Rambach , Akram Youssry , Marco Tomamichel , Jacquiline Romero

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from…

When working with quantum states, analysis of the final quantum state generated through probabilistic measurements is essential. This analysis is typically conducted by constructing the density matrix from either partial or full tomography…

Quantum Physics · Physics 2025-01-14 Rohit Prasad , Pratyay Ghosh , Ronny Thomale , Tobias Huber-Loyola

Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct with high fidelity,…

Quantum Physics · Physics 2022-12-21 Rishabh Gupta , Manas Sajjan , Raphael D. Levine , Sabre Kais

Quantum state tomography (QST) is the procedure for reconstructing unknown quantum states from a series of measurements of different observables. Depending on the physical system, different sets of observables have been used for this…

Quantum Physics · Physics 2023-03-02 Jingfu Zhang , Swathi S. Hegde , Dieter Suter

Quantum state tomography (QST) is an essential technique for characterizing quantum states. However, practical implementations of QST are significantly challenged by factors such as shot noise, attenuation, and Raman scattering, especially…

Quantum Physics · Physics 2024-11-26 Artur Czerwinski

Quantum state tomography, the ability to deduce the state of a quantum system from measured data, is the gold standard for verification and benchmarking of quantum devices. It has been realized in systems with few components, but for larger…

Quantum state tomography aims to estimate the state of a quantum mechanical system which is described by a trace one, Hermitian positive semidefinite complex matrix, given a set of measurements of the state. Existing works focus on…

Quantum Physics · Physics 2023-03-22 Siva Shanmugam , Sheetal Kalyani

Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood generative models with importance sampling, but practical…

Machine Learning · Computer Science 2026-05-29 Weilong Chen , Bojun Zhao , Jan Eckwert , Julija Zavadlav
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