RESUMO
Building a quantum computer is a daunting challenge since it requires good control but also good isolation from the environment to minimize decoherence. It is therefore important to realize quantum gates efficiently, using as few operations as possible, to reduce the amount of required control and operation time and thus improve the quantum state coherence. Here we propose a superconducting circuit for implementing a tunable system consisting of a qutrit coupled to two qubits. This system can efficiently accomplish various quantum information tasks, including generation of entanglement of the two qubits and conditional three-qubit quantum gates, such as the Toffoli and Fredkin gates. Furthermore, the system realizes a conditional geometric gate which may be used for holonomic (non-adiabatic) quantum computing. The efficiency, robustness and universality of the presented circuit makes it a promising candidate to serve as a building block for larger networks capable of performing involved quantum computational tasks.
RESUMO
Semiconductor qubits rely on the control of charge and spin degrees of freedom of electrons or holes confined in quantum dots. They constitute a promising approach to quantum information processing, complementary to superconducting qubits. Here, we demonstrate coherent coupling between a superconducting transmon qubit and a semiconductor double quantum dot (DQD) charge qubit mediated by virtual microwave photon excitations in a tunable high-impedance SQUID array resonator acting as a quantum bus. The transmon-charge qubit coherent coupling rate (~21 MHz) exceeds the linewidth of both the transmon (~0.8 MHz) and the DQD charge qubit (~2.7 MHz). By tuning the qubits into resonance for a controlled amount of time, we observe coherent oscillations between the constituents of this hybrid quantum system. These results enable a new class of experiments exploring the use of two-qubit interactions mediated by microwave photons to create entangled states between semiconductor and superconducting qubits.
RESUMO
Statistical analysis of cost data is often difficult because of highly skewed data resulting from a few patients who incur high costs relative to the majority of patients. When the objective is to predict the cost for an individual patient, the literature suggests that one should choose a regression model based on the quality of its predictions. In exploring the econometric issues, the objective of this study was to estimate a cost function in order to estimate the annual health care cost of dementia. Using different models, health care costs were regressed on the degree of dementia, sex, age, marital status and presence of any co-morbidity other than dementia. Models with a log-transformed dependent variable, where predicted health care costs were re-transformed to the unlogged original scale by multiplying the exponential of the expected response on the log-scale with the average of the exponentiated residuals, were part of the considered models. The root mean square error (RMSE), the mean absolute error (MAE) and the Theil U-statistic criteria were used to assess which model best predicted the health care cost. Large values on each criterion indicate that the model performs poorly. Based on these criteria, a two-part model was chosen. In this model, the probability of incurring any costs was estimated using a logistic regression, while the level of the costs was estimated in the second part of the model. The choice of model had a substantial impact on the predicted health care costs, e.g. for a mildly demented patient, the estimated annual health care costs varied from DKK 71 273 to DKK 90 940 (US$ 1 = DKK 7) depending on which model was chosen. For the two-part model, the estimated health care costs ranged from DKK 44714, for a very mildly demented patient, to DKK 197 840, for a severely demented patient.
