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We generalize the concept of mutually unbiased bases (MUB) to measurements which are not necessarily described by rank one projectors. As such, these measurements can be a useful tool to study the long standing problem of the existence of…

Quantum Physics · Physics 2015-06-18 Amir Kalev , Gilad Gour

The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the…

Quantum Physics · Physics 2022-08-17 Dax Enshan Koh , Sabee Grewal

In quantum mechanics, mutually unbiased bases (MUBs) represent orthonormal bases that are as "far apart" as possible, and their classification reveals rich underlying geometric structure. Given a complex inner product space, we construct…

Mathematical Physics · Physics 2025-08-22 Amit Te'eni , Eliahu Cohen

Accurately estimating the properties of quantum systems is a central challenge in quantum computing and quantum information. We propose a method to obtain unbiased estimators of multiple observables with low statistical error by…

Quantum Physics · Physics 2025-07-25 Stefano Mangini , Daniel Cavalcanti

Mutually unbiased bases (MUB) are an elusive discrete structure in Hilbert spaces. Many (complete sets of) MUB are group covariant, but little is known whether they can be sharply covariant in the sense that the generating groups can have…

Quantum Physics · Physics 2015-03-03 Huangjun Zhu

A scalable Bayesian machine learning framework is introduced for estimating scalar properties of an unknown quantum state from measurement data, which bypasses full density matrix reconstruction. This work is the first to integrate the…

Quantum Physics · Physics 2025-12-08 Hyunho Cha , Wonjung Kim , Jungwoo Lee

Mutually unbiased measurements are a generalization of mutually unbiased bases in which the measurement operators need not to be rank one projectors. In a $d$-dimension space, the purity of measurement elements ranges from $1/d$ for the…

Quantum Physics · Physics 2021-11-30 Mahdi Salehi , Seyed Javad Akhtarshenas , Mohsen Sarbishaei , Hakimeh Jaghouri

Predicting properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few…

Quantum Physics · Physics 2021-04-20 Hsin-Yuan Huang , Richard Kueng , John Preskill

Mutually Unbiased Bases (MUBs) are closely connected with quantum physics, and the structure has a rich mathematical background. We provide equivalent criteria for extending a set of MUBs for $C^n$ by studying real points of a certain…

Discrete Mathematics · Computer Science 2025-07-04 Arindam Banerjee , Kanoy Kumar Das , Ajeet Kumar , Rakesh Kumar , Subhamoy Maitra

We provide a general machine learning methodology that integrates classical shadow representations with unsupervised principal component analysis (PCA) to explore various quantum phase transitions. By sampling spin configurations from…

Quantum Physics · Physics 2026-04-22 Chi-Ting Ho , Daw-Wei Wang

Estimation of expectation values of incompatible observables is an essential practical task in quantum computing, especially for approximating energies of chemical and other many-body quantum systems. In this work we introduce a method for…

Quantum Physics · Physics 2023-03-14 Daniel McNulty , Filip B. Maciejewski , Michał Oszmaniec

Mutually Unbiased bases has various application in quantum information procession and coding theory. There can be maximum d + 1 MUBs in C^d and d/2 +1 MUBs in R^d. But , over R^d MUBs are known to be non existent when d is odd and for most…

Quantum Physics · Physics 2025-12-16 Ajeet Kumar , Uditanshu Sadual

Shadow tomography is a framework for constructing succinct descriptions of quantum states using randomized measurement bases, called classical shadows, with powerful methods to bound the estimators used. We recast existing experimental…

Interfacing quantum and classical processors is an important subroutine in full-stack quantum algorithms. The so-called "classical shadow" method efficiently extracts essential classical information from quantum states, enabling the…

Quantum Physics · Physics 2025-09-11 Shuowei Ma , Junyu Liu

Classical shadow estimation (CSE) is a powerful tool for learning the properties of quantum states and quantum processes. Here we consider the CSE task for quantum unitary channels. By querying an unknown unitary channel $\mathcal{U}$…

Quantum Physics · Physics 2025-12-30 Zihao Li , Changhao Yi , You Zhou , Huangjun Zhu

Analysis of state reconstruction both classical and quantum mechanical on equal footing is outlined. The meaning of "mutual unbiased bases" (MUB) of Hilbert spaces is explained in detail. An alternative quantum state reconstruction, that…

Quantum Physics · Physics 2009-12-31 M. Revzen

We study classical shadows protocols based on randomized measurements in $n$-qubit entangled bases, generalizing the random Pauli measurement protocol ($n = 1$). We show that entangled measurements ($n\geq 2$) enable nontrivial and…

Quantum Physics · Physics 2024-03-27 Matteo Ippoliti

Accurately estimating expectation values of quantum observables with as few measurements as possible is crucial to many quantum computing applications. We introduce a framework that covers many of existing measurement strategies and…

"Classical shadows" are estimators of an unknown quantum state, constructed from suitably distributed random measurements on copies of that state [Nature Physics 16, 1050-1057]. Here, we analyze classical shadows obtained using random…

Quantum Physics · Physics 2023-11-28 Kianna Wan , William J. Huggins , Joonho Lee , Ryan Babbush

Quantum measurements based on mutually unbiased bases are commonly used in quantum information processing, as they are generally viewed as being maximally incompatible and complementary. Here we quantify precisely the degree of…

Quantum Physics · Physics 2020-04-20 Sébastien Designolle , Paul Skrzypczyk , Florian Fröwis , Nicolas Brunner