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This paper considers multiple binary hypothesis tests with adaptive allocation of sensing resources from a shared budget over a small number of stages. A Bayesian formulation is provided for the multistage allocation problem of minimizing…

Methodology · Statistics 2014-11-05 Dennis Wei

Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end,…

The quality of an estimated nonlinear model highly depends on the data quality that was used for the system identification. By using a Gaussian Process-based optimal input design approach, a so-called space-filling dataset can be generated…

Systems and Control · Electrical Eng. & Systems 2026-05-13 Máté Kiss , Maarten Schoukens , Roland Tóth

Simulation models often have parameters as input and return outputs to understand the behavior of complex systems. Calibration is the process of estimating the values of the parameters in a simulation model in light of observed data from…

Methodology · Statistics 2024-11-15 Özge Sürer

The Josephson junction phase qubit has been shown to be a viable candidate for quantum computation. In recent years, the two coupled phase system has been extensively studied theoretically and experimentally. We have analyzed the quantum…

Superconductivity · Physics 2009-08-21 Zechariah E. Thrailkill , S. T. Kennerly , R. C. Ramos

Modern hybrid superconductor-semiconductor Josephson junction arrays are a promising platform for analog quantum simulations. Their controllable and non-sinusoidal energy/phase relation opens the path to implement nontrivial interactions…

Mesoscale and Nanoscale Physics · Physics 2024-06-13 Lorenzo Maffi , Niklas Tausendpfund , Matteo Rizzi , Michele Burrello

Projective measurements of a single two-level quantum mechanical system (a qubit) evolving under a time-independent Hamiltonian produce a probability distribution that is periodic in the evolution time. The period of this distribution is an…

Quantum Physics · Physics 2012-06-05 Christopher Ferrie , Christopher E. Granade , D. G. Cory

Minimizing the number of patients exposed to potentially harmful drugs in early onco logical trials is a major concern during planning. Adaptive designs account for the inherent uncertainty about the true effect size by determining the…

Applications · Statistics 2016-05-03 Kevin Kunzmann , Meinhard Kieser

With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible. Therefore, there is an increasing demand for an efficient continuous monitoring…

Machine Learning · Computer Science 2023-04-04 Runzhe Wan , Yu Liu , James McQueen , Doug Hains , Rui Song

We consider the problem of designing experiments for the comparison of two regression curves describing the relation between a predictor and a response in two groups, where the data between and within the group may be dependent. In order to…

Statistics Theory · Mathematics 2021-01-15 Kirsten Schorning , Holger Dette

Recent advances in superconducting circuit technology have made the fabrication of large, customizable circuits routine. This has led to their application to areas beyond quantum information and, in particular, to their use as quantum…

Quantum Physics · Physics 2025-03-04 Kamal Sharma , Wade DeGottardi

Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past…

Methodology · Statistics 2023-02-03 Nicholas W. Barendregt , Emily G. Webb , Zachary P. Kilpatrick

The measurement of the escape time of a Josephson junction might be used to detect the presence of a sinusoidal signal embedded in noise when standard signal processing tools can be prohibitive. We show that the prescriptions for the…

Instrumentation and Detectors · Physics 2015-06-03 Paolo Addesso , Giovanni Filatrella , Vincenzo Pierro

Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a nonlinear model in these factors. This nonlinear model can be mechanistic, empirical or a hybrid of the two. Motivated by…

Computation · Statistics 2018-10-09 Yuanzhi Huang , Steven Gilmour , Kalliopi Mylona , Peter Goos

Superconductor digital electronics using Josephson junctions as ultrafast switches and magnetic-flux encoding of information was proposed over 30 years ago as a sub-terahertz clock frequency alternative to semiconductor electronics based on…

Superconductivity · Physics 2016-06-03 Sergey K. Tolpygo

In this paper we apply a two-stage sequential design to item calibration problems under a three-parameter logistic model assumption. The measurement errors of the estimates of the latent trait levels of examinees are considered in our…

Applications · Statistics 2013-05-23 Yuan-chin Ivan Chang

The design of nonlinear superconducting quantum circuits often relies on time-consuming iterative electromagnetic simulations requiring manual intervention. These interventions entail, for example, adjusting design variables such as…

We experimentally investigate the stochastic phase dynamics of planar Josephson junctions (JJs) and superconducting quantum interference devices (SQUIDs) defined in epitaxial InAs/Al heterostructures, and characterized by a large ratio of…

Mesoscale and Nanoscale Physics · Physics 2023-04-27 D. Z. Haxell , E. Cheah , F. Křížek , R. Schott , M. F. Ritter , M. Hinderling , W. Belzig , C. Bruder , W. Wegscheider , H. Riel , F. Nichele

Superconducting circuits offer tremendous design flexibility in the quantum regime culminating most recently in the demonstration of few qubit systems supposedly approaching the threshold for fault-tolerant quantum information processing.…

Mesoscale and Nanoscale Physics · Physics 2016-03-25 Yun-Pil Shim , Charles Tahan

Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment. Sequential BED, as opposed to static BED, considers the…

Machine Learning · Statistics 2020-03-23 Steven Kleinegesse , Christopher Drovandi , Michael U. Gutmann
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