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The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained…

Computer Vision and Pattern Recognition · Computer Science 2017-07-25 Wuzhen Shi , Feng Jiang , Shengping Zhang , Debin Zhao

Real-time sensing of ultra-wideband radio-frequency signal with high frequency resolution is challenging, which is confined by the sampling rate of electronic analog-to-digital converter and the capability of digital signal processing. By…

New techniques based on weak measurements have recently been introduced to the field of quantum state reconstruction. Some of them allow the direct measurement of each matrix element of an unknown density operator and need only $O(d)$…

Quantum Physics · Physics 2019-01-31 Luca Calderaro , Giulio Foletto , Daniele Dequal , Paolo Villoresi , Giuseppe Vallone

We present a compressive quantum process tomography scheme that fully characterizes any rank-deficient completely-positive process with no a priori information about the process apart from the dimension of the system on which the process…

Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a…

Machine Learning · Computer Science 2020-03-20 Shaojie Xu , Sihan Zeng , Justin Romberg

The reversible transfer of the quantum information between a photon, an information carrier, and a quantum memory with high fidelity and reliability is the prerequisite for realizing a long-distance quantum communication and a quantum…

We consider a compressed sensing problem in which both the measurement and the sparsifying systems are assumed to be frames (not necessarily tight) of the underlying Hilbert space of signals, which may be finite or infinite dimensional. The…

Information Theory · Computer Science 2020-10-15 Giovanni S. Alberti , Matteo Santacesaria

We propose an experimentally feasible scheme for generating a two $2\times4\times4$ dimensional photons hyperentangled state, entangled in polarization, frequency and spatial mode. This scheme is mainly based on a parametric down-conversion…

Quantum Physics · Physics 2013-04-30 Hong-Bo Xu , Kun Du , Cong-Feng Qiao

Quantum state tomography is a fundamental tool in quantum information processing. It allows us to estimate the state of a quantum system by measuring different observables on many identically prepared copies of the system. This is, in…

Quantum Physics · Physics 2011-11-28 Carlos A. Riofrío

High-dimensional entanglement is a valuable resource for quantum communication, and photon pairs entangled in orbital angular momentum are commonly used for encoding high-dimensional quantum states. However, methods for preparation of…

Quantum Physics · Physics 2018-12-19 Egor Kovlakov , Stanislav Straupe , Sergey Kulik

The possible state space dimension increases exponentially with respect to the number of qubits. This feature makes the quantum state tomography expensive and impractical for identifying the state of merely several qubits. The recent…

Information Theory · Computer Science 2014-01-28 Kezhi Li , Shuang Cong

In engineered quantum systems, the Hamiltonian is often not completely known and needs to be determined experimentally with accuracy and efficiency. We show that this may be done at temperatures that are greater than the characteristic…

Quantum Physics · Physics 2015-11-25 Kenneth Rudinger , Robert Joynt

We discuss the data-pattern tomography for reconstruction of entangled states of light. We show that for a moderate number of probe coherent states it is possible to achieve high accuracy of representation not only for single-mode states…

Quantum Physics · Physics 2017-01-19 Vadim Reut , Alexander Mikhalychev , Dmitri Mogilevtsev

Entanglement detection is one of the most conventional tasks in quantum information processing. While most experimental demonstrations of high-dimensional entanglement rely on fidelity-based witnesses, these are powerless to detect…

In quantum information transformation and quantum computation, the most critical issues are security and accuracy. These features, therefore, stimulate research on quantum state characterization. A characterization tool, Quantum state…

Quantum Physics · Physics 2023-06-01 Xudan Chai , Teng Ma , Qihao Guo , Zhangqi Yin , Hao Wu , Qing Zhao

This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals…

Information Theory · Computer Science 2015-03-19 Jorge Silva , Minhua Chen , Yonina C. Eldar , Guillermo Sapiro , Lawrence Carin

Is it possible to detect a feature in an image without ever looking at it? Images are known to have sparser representation in Wavelets and other similar transforms. Compressed Sensing is a technique which proposes simultaneous acquisition…

Image and Video Processing · Electrical Eng. & Systems 2020-06-09 Suyash Shandilya

We have performed the first experimental tomographic reconstruction of a three-photon polarization state. Quantum state tomography is a powerful tool for fully describing the density matrix of a quantum system. We measured 64 three-photon…

Quantum Physics · Physics 2009-11-10 Kevin J. Resch , Philip Walther , Anton Zeilinger

Quantum computers can revolutionize science and technology, but their realization remains challenging across all platforms. A promising route to scalability is photonic measurement-based quantum computation, where single-qubit measurements…

Quantum Physics · Physics 2024-09-17 Ohad Lib , Yaron Bromberg

Compressed sensing allows for the recovery of sparse signals from few measurements, whose number is proportional to the sparsity of the unknown signal, up to logarithmic factors. The classical theory typically considers either random linear…

Functional Analysis · Mathematics 2025-04-02 Giovanni S. Alberti , Alessandro Felisi , Matteo Santacesaria , S. Ivan Trapasso
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