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Schemes of classical shadows have been developed to facilitate the read-out of digital quantum devices, but similar tools for analog quantum simulators are scarce and experimentally impractical. In this work, we provide a measurement scheme…

Quantum Physics · Physics 2024-12-18 Janek Denzler , Antonio Anna Mele , Ellen Derbyshire , Tommaso Guaita , Jens Eisert

Detecting entanglement in multipartite quantum states is an inherently probabilistic process, typically with a few measured samples. The level of confidence in entanglement detection quantifies the scheme's validity via the probability that…

Many applications of quantum simulation require to prepare and then characterize quantum states by performing an efficient partial tomography to estimate observables corresponding to $k$-body reduced density matrices ($k$-RDMs). For…

Quantum Physics · Physics 2020-09-24 Xavier Bonet-Monroig , Ryan Babbush , Thomas E. O'Brien

Understanding the structure of quantum correlations in a many-body system is key to its computational treatment. For fermionic systems, correlations can be defined as deviations from Slater determinant states. The link between fermionic…

Quantum Physics · Physics 2025-04-16 Mykola Semenyakin , Yevheniia Cheipesh , Yaroslav Herasymenko

Adaptive tomography has been widely investigated to achieve faster state tomography processing of quantum systems. Infidelity of the nearly pure states in a quantum information process generally scales as O(1/sqrt(N) ), which requires a…

Quantum Physics · Physics 2023-05-09 Hyeok Hwang , JaeKyung Choi , Eunseong Kim

Experimental procedures are presented for the rapid detection of entanglement of unknown arbitrary quantum states. The methods are based on the entanglement criterion using accessible correlations and the principle of correlation…

Solving interacting fermionic quantum many-body problems as they are ubiquitous in quantum chemistry and materials science is a central task of theoretical and numerical physics, a task that can commonly only be addressed in the sense of…

Quantum Physics · Physics 2024-10-14 Christian Krumnow , Zoltán Zimborás , Jens Eisert

We study the concept of entanglement distance between two quantum states which quantifies the amount of information shared between their reduced density matrices (RDMs). Using analytical arguments combined with…

Strongly Correlated Electrons · Physics 2017-10-18 Mohammad-Sadegh Vaezi , Abolhassan Vaezi

The field theory of nonrelativistic fermions interacting via contact interactions can be used to calculate the properties of few-body systems of cold atoms confined in harmonic traps. The state-operator correspondence of Non-Relativistic…

Other Condensed Matter · Physics 2009-11-13 Thomas Mehen

In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input…

Systems and Control · Electrical Eng. & Systems 2021-07-29 Emily A. Reed , Guilherme Ramos , Paul Bogdan , Sérgio Pequito

We study the classic Text-to-Pattern Hamming Distances problem: given a pattern $P$ of length $m$ and a text $T$ of length $n$, both over a polynomial-size alphabet, compute the Hamming distance between $P$ and $T[i\, .\, . \, i+m-1]$ for…

Data Structures and Algorithms · Computer Science 2024-12-20 Timothy M. Chan , Ce Jin , Virginia Vassilevska Williams , Yinzhan Xu

Self-learning Monte Carlo method [arXiv:1610.03137, 1611.09364] is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we implement this method in the framework of determinantal…

Strongly Correlated Electrons · Physics 2018-07-12 Xiao Yan Xu , Yang Qi , Junwei Liu , Liang Fu , Zi Yang Meng

A filter for inertial-based odometry is a recursive method used to estimate the pose from measurements of ego-motion and relative pose. Currently, there is no known filter that guarantees the computation of a globally optimal solution for…

Robotics · Computer Science 2024-02-08 Xinghan Li , Haoying Li , Guangyang Zeng , Qingcheng Zeng , Xiaoqiang Ren , Chao Yang , Junfeng Wu

Quantum state learning is a fundamental problem in physics and computer science. As near-term quantum devices are error-prone, it is important to design error-resistant algorithms. Apart from device errors, other unexpected factors could…

Quantum Physics · Physics 2025-08-20 Maryam Aliakbarpour , Vladimir Braverman , Nai-Hui Chia , Yuhan Liu

Fermion sampling is to generate probability distribution of a many-body Slater-determinant wavefunction, which is termed "determinantal point process" in statistical analysis. For its inherently-embedded Pauli exclusion principle, its…

Quantum Physics · Physics 2023-01-31 Haoran Sun , Jie Zou , Xiaopeng Li

We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( ( 1 / k ) \log D )$ rate, where $k$ denotes the number of…

Quantum Physics · Physics 2021-10-05 Chien-Ming Lin , Hao-Chung Cheng , Yen-Huan Li

A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve characterizing the pattern, leading together to a…

Statistics Theory · Mathematics 2016-08-15 Rainer Dahlhaus , Thierry Dumont , Sylvain Le Corff , Jan C. Neddermeyer

Fermionic linear optics (FLO) with Gaussian resources is efficiently classically simulable. We show that this is no longer the case for such quantum circuits for fermions with internal degrees of freedom, equipped with mid-circuit number…

Quantum Physics · Physics 2026-03-27 Chenfeng Cao , Yifan Tang , Jens Eisert

Fermionic linear optics is a limited form of quantum computation which is known to be efficiently simulable on a classical computer. We revisit and extend this result by enlarging the set of available computational gates: in addition to…

Quantum Physics · Physics 2013-10-04 Sergey Bravyi , Robert Koenig

Density functional theory underlies the most successful and widely used numerical methods for electronic structure prediction of solids. However, it has the fundamental shortcoming that the universal density functional is unknown. In…

Disordered Systems and Neural Networks · Physics 2020-09-23 M. Michael Denner , Mark H. Fischer , Titus Neupert
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