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Measurement in quantum simulations provides a means for extracting meaningful information from a complex quantum state, and for quantum computing reducing the complexity of measurement will be vital for near-term applications. For most…

Quantum Physics · Physics 2021-05-11 Scott E. Smart , David A. Mazziotti

In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a…

Quantum Physics · Physics 2016-05-12 Christopher Granade , Joshua Combes , D. G. Cory

Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows…

Quantum Physics · Physics 2023-06-27 Leopoldo Sarra , Florian Marquardt

Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which…

Recently quantum tomography has been proposed as a fundamental tool for prototyping a few qubit quantum device. It allows the complete reconstruction of the state produced from a given input into the device. From this reconstructed density…

Quantum Physics · Physics 2009-11-07 R. T. Thew , K. Nemoto , A. G. White , W. J. Munro

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

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for…

We expand the scope of the statistical notion of error probability, i.e., how often large deviations are observed in an experiment, in order to make it directly applicable to quantum tomography. We verify that the error probability can…

Quantum Physics · Physics 2011-01-24 Takanori Sugiyama , Peter S. Turner , Mio Murao

The quantum state of a light beam can be represented as an infinite dimensional density matrix or equivalently as a density on the plane called the Wigner function. We describe quantum tomography as an inverse statistical problem in which…

Statistics Theory · Mathematics 2007-06-13 L. M. Artiles , R. D. Gill , M. I. Guta

Establishing a notion of the quantum state that applies consistently across space and time could be a crucial step toward formulating a relativistic quantum theory. We give an operational meaning to multipartite quantum states over…

Quantum Physics · Physics 2026-04-14 Seok Hyung Lie , Hyukjoon Kwon

Quantum information systems are on a path to vastly exceed the complexity of any classical device. The number of entangled qubits in quantum devices is rapidly increasing and the information required to fully describe these systems scales…

Quantum computers promise to perform computations beyond the reach of modern computers with profound implications for scientific research. Due to remarkable technological advances, small scale devices are now becoming available for use. One…

Strongly Correlated Electrons · Physics 2022-05-20 Adam Smith , Bernhard Jobst , Andrew G. Green , Frank Pollmann

With nowadays steadily growing quantum processors, it is required to develop new quantum tomography tools that are tailored for high-dimensional systems. In this work, we describe such a computational tool, based on recent ideas from…

We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate…

Quantum Physics · Physics 2020-06-09 Sanjaya Lohani , Brian T. Kirby , Michael Brodsky , Onur Danaci , Ryan T. Glasser

Quantum phase classification is a fundamental problem in quantum many-body physics, traditionally approached using order parameters or quantum machine learning techniques such as quantum convolutional neural networks (QCNNs). However, these…

Quantum Physics · Physics 2026-02-05 Akira Tanji , Hiroshi Yano , Naoki Yamamoto

Quantum states and measurements exhibit wave-like --- continuous, or particle-like --- discrete, character. Hybrid discrete-continuous photonic systems are key to investigating fundamental quantum phenomena, generating superpositions of…

Quantum metrology based on quantum entanglement and quantum coherence improves the accuracy of measurement. In this paper, we briefly review the schemes of quantum metrology in various complex systems, including non-Markovian noise,…

Quantum Physics · Physics 2024-01-18 Qing Ai , Yang-Yang Wang , Jing Qiu

We explore a previously unknown connection between two important problems in physics, i.e., quantum macroscopicity and the quantum phase transition. We devise a general and computable measure of quantum macroscopicity that can be applied to…

Quantum Physics · Physics 2016-11-30 Chae-Yeun Park , Minsu Kang , Chang-Woo Lee , Jeongho Bang , Seung-Woo Lee , Hyunseok Jeong

Measuring the state of quantum computers is a highly non-trivial task, with implications for virtually all quantum algorithms. We propose a novel scheme where identical copies of a quantum state are measured jointly so that all Pauli…

Quantum Physics · Physics 2025-09-03 Rick P. A. Simon , Zheng Shi , Charlie Nation , Andrew Jena , Luca Dellantonio

Quantum state tomography suffers from the measurement effort increasing exponentially with the number of qubits. Here, we demonstrate permutationally invariant tomography for which, contrary to conventional tomography, all resources scale…