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In this work, we report on a novel quantum state reconstruction process based on the disentanglement algorithm. Using variational quantum circuits, we disentangle the quantum state to a product of computational zero states. Inverse…

Quantum Physics · Physics 2024-11-08 Juan Yao

Computing entanglement of an arbitrary bipartite or multipartite mixed state is in general not an easy task as it usually involves complex optimization. Here we show that exploiting symmetries of certain mixed states, we can compute a…

Quantum Physics · Physics 2016-08-31 Tamoghna Das , Sudipto Singha Roy , Shrobona Bagchi , Avijit Misra , Aditi Sen De , Ujjwal Sen

We revisit the problem of detection of entanglement of an unknown two-qubit state using minimal resources. Using weak values and just two copies of an arbitrary two-qubit state, we present a protocol where a post selection measurement in…

Quantum Physics · Physics 2019-01-17 Suchetana Goswami , Sagnik Chakraborty , Sibasish Ghosh , A. S. Majumdar

Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous…

In quantum information theory, the reliable and effective detection of entanglement is of paramount importance. However, given an unknown state, assessing its entanglement is a challenging task. To attack this problem, we investigate the…

Quantum Physics · Physics 2015-12-09 Jochen Szangolies , Hermann Kampermann , Dagmar Bruß

The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, achieving this goal requires either a priori knowledge on the system or…

We present a conjugate gradient method for calculating the entanglement of formation of arbitrary mixed quantum states of any dimension and with any bipartite division of the Hilbert space. The development of the gradient used by the…

Quantum Physics · Physics 2007-05-23 J. R. Gittings , A. J. Fisher

Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science. Recent developments have shown that a modest number of randomized measurements suffices to learn many properties of a…

Quantum Physics · Physics 2022-12-13 Katherine Van Kirk , Jordan Cotler , Hsin-Yuan Huang , Mikhail D. Lukin

Classifying states as entangled or separable is a highly challenging task, while it is also one of the foundations of quantum information processing theory. This task is higly nontrivial even for relatively simple cases, such as two-qutrit…

Quantum Physics · Physics 2022-11-08 Marcin Wieśniak

Compressed sensing is a signal processing method that acquires data directly in a compressed form. This allows one to make less measurements than what was considered necessary to record a signal, enabling faster or more precise measurement…

Statistical Mechanics · Physics 2012-08-20 Florent Krzakala , Marc Mézard , François Sausset , Yifan Sun , Lenka Zdeborová

Randomized measurements constitute a simple measurement primitive that exploits the information encoded in the outcome statistics of samples of local quantum measurements defined through randomly selected bases. In this work we exploit the…

Quantum Physics · Physics 2023-06-08 Sophia Ohnemus , Heinz-Peter Breuer , Andreas Ketterer

Group theoretic methods to construct all N-particle singlet states by iterative recursion are presented. These techniques are applied to the quantum correlations of four spin one-half particles in their singlet states. Multipartite…

Quantum Physics · Physics 2009-01-29 Maria Schimpf , Karl Svozil

We present a theoretical analysis of methods to synthesize entangled states of two superconducting resonators. These methods use experimentally demonstrated interactions of resonators with artificial atoms, and offer efficient routes to…

Quantum Physics · Physics 2013-05-30 Frederick W. Strauch , Douglas Onyango , Kurt Jacobs , Raymond W. Simmonds

We propose a method for reconstruction of the density matrix from measurable time-dependent (probability) distributions of physical quantities. The applicability of the method based on least-squares inversion is - compared with other…

Quantum Physics · Physics 2016-09-08 T. Opatrny , D. -G. Welsch , W. Vogel

The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling clearly limits our ability to do tomography to systems with no more than a few qubits and has been used to…

Representation learning methods are an important tool for addressing the challenges posed by complex observations spaces in sequential decision making problems. Recently, many methods have used a wide variety of types of approaches for…

Machine Learning · Computer Science 2025-06-24 Ayoub Echchahed , Pablo Samuel Castro

The detection and classification of entanglement properties in a two-qubit and a multi-qubit system is a topic of great interest. This topic has been extensively studied, and as a result, we discovered various approaches for detecting and…

Quantum Physics · Physics 2023-05-29 Anu Kumari

In this work, we present a practical and efficient framework for verifying entangled states when only a tomographically incomplete measurement setting is available-specifically, when access to observables is severely limited. We show how…

Self-testing is a powerful device-independent technique that enables one to deduce the forms of both the quantum state and the measurements involved in a physical experiment based solely on observed correlations. Although numerous schemes…

Quantum Physics · Physics 2025-08-22 Arturo Konderak , Wojciech Bruzda , Remigiusz Augusiak

Learning properties of quantum states and channels is known to benefit from resources such as entangled operations, auxiliary qubits, and adaptivity, whereas the resource structure of measurement learning, namely, learning properties of…

Quantum Physics · Physics 2026-02-26 Zhenhuan Liu , Qi Ye , Zhenyu Cai , Jens Eisert
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