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Bosonic modes are prevalent in all aspects of quantum information processing. However, existing tools for characterizing the quality, stability, and noise properties of bosonic modes are limited, especially in a driven setting. Here, we…

Quantum Physics · Physics 2025-06-13 Christophe H. Valahu , Tomas Navickas , Michael J. Biercuk , Ting Rei Tan

The utility of near-term quantum computers and simulators is likely to rely upon software-hardware co-design, with error-aware algorithms and protocols optimized for the platforms they are run on. Here, we show how knowledge of noise in a…

Quantum Physics · Physics 2024-09-04 Kushal Seetharam , Dries Sels , Eugene Demler

We obtain minimax-optimal convergence rates in the supremum norm, including information-theoretic lower bounds, for estimating the covariance kernel of a stochastic process which is repeatedly observed at discrete, synchronous design…

Statistics Theory · Mathematics 2025-09-03 Max Berger , Hajo Holzmann

Probabilistic error cancellation is an attempt to reverse the effect of dissipative noise channels on quantum computers by applying unphysical channels after the execution of a quantum algorithm on noisy hardware. We investigate on general…

With a finite amount of measurement data acquired in variational quantum algorithms, the statistical benefits of several optimized numerical estimation schemes, including the scaled parameter-shift (SPS) rule and finite-difference (FD)…

Quantum Physics · Physics 2024-08-07 Yong Siah Teo

We present a supervised machine learning-based method using convolutional neural networks to estimate the covariance matrix of Gaussian quantum states in the presence of thermal noise. Unlike computationally intensive density matrix…

In this paper the influence of large-scale decreasing and increasing gradients of the density of magnetized plasma on the relaxation process of a continuously injected relativistic electron beam with an energy of 611 keV ($v_b=0.9c$) and a…

Plasma Physics · Physics 2022-08-30 Vladimir Annenkov , Evgeniia Volchok

Quantum neural networks (QNNs) encounter significant challenges in realizing nonlinear behavior and effectively optimizing parameters. This study addresses these issues by modeling nonlinearity through a Taylor series expansion, where the…

Quantum Physics · Physics 2025-04-22 Ding-Dang Yang

The density estimation is one of the core problems in statistics. Despite this, existing techniques like maximum likelihood estimation are computationally inefficient due to the intractability of the normalizing constant. For this reason an…

Machine Learning · Computer Science 2021-01-14 Tsimboy Olga , Yermek Kapushev , Evgeny Burnaev , Ivan Oseledets

Noise is the main source that hinders us from fully exploiting quantum advantages in various quantum informational tasks. However, characterizing and calibrating the effect of noise is not always feasible in practice. Especially for quantum…

Quantum Physics · Physics 2024-03-12 Hyukgun Kwon , Changhun Oh , Youngrong Lim , Hyunseok Jeong , Liang Jiang

Quantum multiparameter estimation involves estimating multiple parameters simultaneously and can be more precise than estimating them individually. Our interest here is to determine fundamental quantum limits to the achievable…

Quantum Physics · Physics 2019-03-26 Shibdas Roy

There is a well known analogy between the Laughlin trial wave function for the fractional quantum Hall effect, and the Boltzmann factor for the two-dimensional one-component plasma. The latter requires analytic continuation beyond the…

Statistical Mechanics · Physics 2015-05-27 T. Can , P. J. Forrester , G. Tellez , P. Wiegmann

We study the fine structure of long-time quantum noise in correlation functions of AdS/CFT systems. Under standard assumptions of quantum chaos for the dynamics and the observables, we estimate the size of exponentially small oscillations…

High Energy Physics - Theory · Physics 2016-01-20 Jose L. F. Barbon , Eliezer Rabinovici

Noise is usually regarded as the main obstacle to achieving a scalable quantum advantage, but recent evidence in quantum reservoir computing [L. Domingo, F. Borondo, and G. G. Carlo. Taking advantage of noise in quantum reservoir computing,…

Quantum Physics · Physics 2026-05-29 J. Montes , F. Borondo , Gabriel G. Carlo

While many advanced statistical methods for the design of experiments exist, it is still typical for physical experiments to be performed adaptively based on human intuition. As a consequence, experimental resources are wasted on…

Methodology · Statistics 2025-03-04 Anton van Beek

We analyze the long time behavior of a quantum computer running a quantum error correction (QEC) code in the presence of a correlated environment. Starting from a Hamiltonian formulation of realistic noise models, and assuming that QEC is…

Quantum Physics · Physics 2008-07-11 E. Novais , Eduardo R. Mucciolo , Harold U. Baranger

Quantum error correcting codes have been shown to have the ability of making quantum information resilient against noise. Here we show that we can use quantum error correcting codes as diagnostics to characterise noise. The experiment is…

Quantum Physics · Physics 2009-11-13 M. Laforest , D. Simon , J. -C. Boileau , J. Baugh , M. Ditty , R. Laflamme

In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly-oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy 2D…

Numerical Analysis · Mathematics 2014-09-16 Gene Katsevich , Alexander Katsevich , Amit Singer

We present a two-state practical quantum bit commitment protocol, the security of which is based on the current technological limitations, namely the nonexistence of either stable long-term quantum memories or nondemolition measurements.…

In this paper we propose a method to estimate the density matrix \rho of a d-level quantum system by measurements on the N-fold system. The scheme is based on covariant observables and representation theory of unitary groups and it extends…

Quantum Physics · Physics 2009-11-10 M. Keyl
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