Solid States Devices at 50 GHZ

Akaike, M.; Kanmuri, N.; Kato, H.

doi:10.1109/gmtt.1973.1123094

Cited by 18 publications

(19 citation statements)

References 1 publication

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“…Thus, it was also relevant to take into account other measures that reward fit to the data but penalize model complexity. In addition to the aforementioned RMSEA, we report values of three information criteria, the Akaike information criterion (AIC; Akaike, 1973), the Bayesian information criterion (BIC; Raftery, 1995; Schwarz, 1978), and a sample-size-adjusted version of the BIC (SABIC; Sclove,1987), to convey the relative fit of both nested and nonnested models. These indices reward fit but also penalize model complexity, operationalized as the number of free parameters estimated by a given model.…”

Section: Methodsmentioning

confidence: 99%

Novel and familiar object recognition rely on the same ability.

Sunday¹,

Tomarken²,

Cho³

et al. 2022

Journal of Experimental Psychology: General

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There is recent evidence for a domain-general object recognition ability, called O, which is distinct from general intelligence and other cognitive and personality constructs. We extend the study of O by characterizing how it generalizes to the ability to recognize familiar objects and to the ability to make judgments of the average identity of ensembles of objects. We applied latent variable modeling to data collected from a sample of adults (N = 284) in three different tasks and for six different object domains (three novel and three familiar). The results replicated prior work in finding that on average 88% of the variance of lower-order factors could be accounted by O for novel objects. The latent constructs recruited by the higher-order factor for novel objects and for familiar objects were almost perfectly correlated and therefore functionally identical. A latent factor for ensemble perception shared about 42% of the variance with O, suggesting at least strong overlap between abilities supporting judgments about individual objects and ensemble of objects. This work extends the theoretical reach of O by showing generalization across two dimensions (familiar vs. novel objects; individual vs. ensemble object perception). With respect to the structure of individual differences in high-level vision, researchers would benefit from accounting for the contribution of O when seeking to understand various domain-specific abilities.

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Section: Methodsmentioning

confidence: 99%

Novel and familiar object recognition rely on the same ability.

Sunday¹,

Tomarken²,

Cho³

et al. 2022

Journal of Experimental Psychology: General

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“…In order to quantify the amount of unitary versus nonunitary errors in the iterative randomized benchmarking data, we fit the data to both quadratic and linear models. Using the Akaike information criterion (AIC), we determine which model most accurately describes the data [17,18]. The AIC is a useful tool for model selection and has been applied to quantum information previously [19].…”

mentioning

confidence: 99%

Characterizing errors on qubit operations via iterative randomized benchmarking

Sheldon¹,

Bishop²,

Magesan³

et al. 2016

Phys. Rev. A

151

119

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With improved gate calibrations reducing unitary errors, we achieve a benchmarked single-qubit gate fidelity of 99.95% with superconducting qubits in a circuit quantum electrodynamics system. We present a method for distinguishing between unitary and non-unitary errors in quantum gates by interleaving repetitions of a target gate within a randomized benchmarking sequence. The benchmarking fidelity decays quadratically with the number of interleaved gates for unitary errors but linearly for non-unitary, allowing us to separate systematic coherent errors from decoherent effects. With this protocol we show that the fidelity of the gates is not limited by unitary errors, but by another drive-activated source of decoherence such as amplitude fluctuations.Accurate characterization of control gates is an essential task for developing any quantum computing device. Quantum process tomography (QPT) [1][2][3] has been the standard method for characterizing quantum gates because, ideally, it produces a full reconstruction of the quantum process. In practice however, QPT suffers from many drawbacks, the most inimical being its exponential scaling in the number of quantum bits (qubits) comprising the system and that it is limited by state preparation and measurement (SPAM) errors. Various methods such as randomized benchmarking (RB) [4][5][6][7] and gate set tomography (GST) [8,9] have recently been developed to help overcome these limitations. RB is both insensitive to SPAM errors and efficient [10]. However, it only extracts a single piece of information, the average gate fidelity. GST on the other hand helps to overcome limitations from SPAM errors by reconstructing an entire library of gates in a self-consistent manner. The price paid for this self-consistent reconstruction is an even worse scaling than QPT.As control calibration techniques continue to improve and quantum gates approach the fidelity required for fault tolerant quantum computation, it becomes both important and difficult to verify the presence of increasingly small errors. Error verification constitutes a critical first step in a debugging routine since different physical mechanisms can lead to different error types. QPT and GST are often poor choices for error verification since they are time consuming and contain so much information that backing out the presence of specific error types on small scales can be a challenge in itself. In addition, SPAM errors in QPT sets a lower limit on the detectable error strengths [8]. At the other end of the spectrum, while standard RB is efficient the information it contains about the gate is typically not enough to perform any sort of useful error verification. An extension of standard RB, interleaved randomized benchmarking, consists of interleaving a target gate in a benchmarking sequence and provides bounds on the error for the gate of interest [11,12]. Interleaved benchmarking can identify gates that are poorly calibrated, but does not reveal if the errors are due to decoherence, over-/underrotations, or offresona...

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“…These calculations are based on von Mises distributions (circular normal distributions) and test simultaneously null and multiple alternative hypotheses. The results of the model calculations for the distributions of β for the various groups were then evaluated on the basis of the Akaike information criterion (AIC), (Akaike, 1973). The AIC was applied to select the optimal model as a measure of uni- or bimodality with the smallest AIC as the best model.…”

Section: Resultsmentioning

confidence: 99%

Generalization and Transfer of Navigation Memory in Honeybees

Bullinger

Greggers

Menzel

2022

Preprint

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Flying insects like the honeybee learn multiple features of the environment for efficient navigation. Here we ask whether the memory of such features is generalized to novel test conditions. Foraging bees from colonies located in 5 different home areas were tested in a common area for their search flights. Harmonic radar was used to track the search flights of the bees. The test area resembled partly or not at all the layout of landmarks in the respective home areas. Random search as the only guide for searching was excluded by two model calculations. The generalization and transfer effect was quantified by a partial least squares regression analysis of the guiding effect of the ground structures in the test area. Classification was performed with a support vector machine in order to account for optimal hyperplanes. A rank order of well separated clusters was found that partly resemble the graded differences between the ground structures of the home areas and the test area. We conclude that foragers guide their search flights by a generalization and transfer process that is based on their acquired navigation memory. The conclusion is discussed in the context of the learning and generalization process in an insect, the honeybee.

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Solid States Devices at 50 GHZ

Cited by 18 publications

References 1 publication

Novel and familiar object recognition rely on the same ability.

Novel and familiar object recognition rely on the same ability.

Characterizing errors on qubit operations via iterative randomized benchmarking

Generalization and Transfer of Navigation Memory in Honeybees

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