The Experts below are selected from a list of 96822 Experts worldwide ranked by ideXlab platform

Hiroaki Terashima - One of the best experts on this subject based on the ideXlab platform.

  • Allowed region and Optimal Measurement for information versus disturbance in quantum Measurements
    Quantum Information Processing, 2017
    Co-Authors: Hiroaki Terashima
    Abstract:

    We present graphs of information versus disturbance for general quantum Measurements of completely unknown states. Each piece of information and disturbance is quantified by two measures: (i) the Shannon entropy and estimation fidelity for the information and (ii) the operation fidelity and physical reversibility for the disturbance. These measures are calculated for a single outcome based on the general formulas derived by the present author (Terashima in Phys Rev A 93:022104, 2016) and are plotted on four types of information–disturbance planes to show their allowed regions. In addition, we discuss the graphs of these metrics averaged over all possible outcomes and the Optimal Measurements when saturating the upper bounds on the information for a given disturbance. The results considerably broaden the perspective of trade-offs between information and disturbances in quantum Measurements.

Sandor Imre - One of the best experts on this subject based on the ideXlab platform.

  • Dense Quantum Measurement Theory
    Scientific Reports, 2019
    Co-Authors: Laszlo Gyongyosi, Sandor Imre
    Abstract:

    Quantum Measurement is a fundamental cornerstone of experimental quantum computations. The main issues in current quantum Measurement strategies are the high number of Measurement rounds to determine a global Optimal Measurement output and the low success probability of finding a global Optimal Measurement output. Each Measurement round requires preparing the quantum system and applying quantum operations and Measurements with high-precision control in the physical layer. These issues result in extremely high-cost Measurements with a low probability of success at the end of the Measurement rounds. Here, we define a novel Measurement for quantum computations called dense quantum Measurement. The dense Measurement strategy aims at fixing the main drawbacks of standard quantum Measurements by achieving a significant reduction in the number of necessary Measurement rounds and by radically improving the success probabilities of finding global Optimal outputs. We provide application scenarios for quantum circuits with arbitrary unitary sequences, and prove that dense Measurement theory provides an experimentally implementable solution for gate-model quantum computer architectures.

Masahito Hayashi - One of the best experts on this subject based on the ideXlab platform.

  • Optimal sequence of quantum Measurements in the sense of stein s lemma in quantum hypothesis testing
    Journal of Physics A, 2002
    Co-Authors: Masahito Hayashi
    Abstract:

    We derive a necessary and sufficient condition for a sequence of quantum Measurements to achieve the Optimal performance in quantum hypothesis testing. We discuss what quantum Measurement we should perform in order to attain the Optimal exponent of the second error probability under the condition that the first error probability goes to 0. As an asymptotically Optimal Measurement, we propose a projection Measurement characterized by the irreducible representation theory of the special linear group SL(). Especially, in the spin-1/2 system, it is realized by the simultaneous Measurement of the total momentum and a momentum of a specified direction. As a by-product, we obtain another proof of quantum Stein's lemma. In addition, an asymptotically Optimal Measurement is constructed in the quantum Gaussian case, and it is physically meaningful.

Julie Martyn - One of the best experts on this subject based on the ideXlab platform.

  • development of the measure of ovarian symptoms and treatment concerns aiming for Optimal Measurement of patient reported symptom benefit with chemotherapy for symptomatic ovarian cancer
    International Journal of Gynecological Cancer, 2014
    Co-Authors: Madeleine King, Martin R Stockler, Phyllis Butow, Rachel Oconnell, Merryn Voysey, Amit M Oza, Kim Gillies, Heidi S Donovan, Rebecca Merciecabebber, Julie Martyn
    Abstract:

    Objective: The aim of this study was to determine the Optimal patient-reported outcome measure (PROM) for assessing symptom benefit in trials of palliative chemotherapy for women with symptomatic ovarian cancer. Methods: Candidate PROMs were EORTC QLQ-C30 plus ovarian-specific QLQ-OV28, Functional Assessment of Cancer Therapy-Ovarian (FACT-O), FACT Ovarian Symptom Index (FOSI), and gynecologic cancer-specific Symptom Representation Questionnaire. Predefined Optimality criteria were inclusion of all symptoms necessary for the specified purpose, recall period covering typical length of palliative chemotherapy, numerical item rating scales, and all necessary symptoms included in a single symptom index. Qualitative and quantitative methods were applied to data from stage 1 of the Gynecologic Cancer Intergroup Symptom Benefit Study to determine the set of necessary symptoms and to objectively assess candidate PROMs against the Optimality criteria. Results: Ten necessary symptoms were identified: pain, fatigue, abdominal bloating/discomfort, sleep disturbance, bowel disturbance, nausea and vomiting, shortness of breath, poor appetite, urinary symptoms, and weight changes. Although QLQ-C30 and QLQ-OV28 together cover all these symptoms, they split them into numerous scales, dissipating potential symptombenefit signal. Conversely, FACT-O does not cover all necessary symptoms and contains many other HRQoL-related items and treatment side effects, diluting potential symptom-benefit signal when summed into scales. Item response scales and composite scoring of all candidate PROMs were subOptimal to our specific purpose. We therefore developed a new PROM, the Measure of Ovarian Symptoms and Treatment (MOST) concerns, to provide Optimal Measurement for the specified purpose. Conclusions: This article documents the development of theMOST, a new PROMdesigned to assess patient-reported benefits and burden as end points in clinical trials of palliative chemotherapy for women with symptomatic ovarian cancer. The validity, reliability, and statistical efficiency of theMOST, relative to the best candidate scales of existing PROMs,will be assessed in the stage 2 of Gynecologic Cancer Intergroup Symptom Benefit Study. Copyright © 2014 by IGCS and ESGO.

Sigurd Skogestad - One of the best experts on this subject based on the ideXlab platform.

  • Optimal Measurement combinations as controlled variables
    Journal of Process Control, 2008
    Co-Authors: Vidar Alstad, Sigurd Skogestad, Eduardo S. Hori
    Abstract:

    Abstract This paper deals with the Optimal selection of linear Measurement combinations as controlled variables, c = Hy . The objective is to achieve “self-optimizing control”, which is when fixing the controlled variables c indirectly gives near-Optimal steady-state operation with a small loss. The nullspace method of Alstad and Skogestad [V. Alstad, S. Skogestad, Null space method for selecting Optimal Measurement combinations as controlled variables, Ind. Eng. Chem. Res. 46 (3) (2007) 846–853] focuses on minimizing the loss caused by disturbances. We here provide an explicit expression for H for the case where the objective is to minimize the combined loss for disturbances and Measurement errors. In addition, we extend the nullspace method to cases with extra Measurements by using the extra degrees of freedom to minimize the loss caused by Measurement errors. Finally, the results are interpreted more generally as deriving linear invariants for quadratic optimization problems.

  • null space method for selecting Optimal Measurement combinations as controlled variables
    Industrial & Engineering Chemistry Research, 2007
    Co-Authors: Vidar Alstad, Sigurd Skogestad
    Abstract:

    The issue in this paper is to select controlled variables c as combinations of the Measurements y. The objective is to obtain self-optimizing control, which is when we can achieve near-Optimal steady-state operation with constant setpoints for the controlled variables, without the need to reoptimize when new disturbances perturb the plant. The null space method yields locally Optimal controlled variables c = Hy that are linear combinations of Measurements y. The requirement is that we at least have as many Measurements as there are unconstrained degrees of freedom, including disturbances, and that the implementation error is neglected. The method is surprisingly simple. From a steady-state model of the plant, the first step is to obtain the Optimal sensitivity matrix F, with respect to the disturbances. The Optimal matrix H satisfies HF = 0; therefore, the next step is to obtain H in the left null space of F. As an illustration, the method is used to obtain temperature combinations for control of a Petlyu...