A tutorial on regularized partial correlation networks

Exploring the interconnectedness of fatigue, depression, anxiety and potential risk and protective factors in cancer patients: a network approach

Researchers have extensively studied fatigue, depression and anxiety in cancer patients. Several risk and protective factors have been identified for these symptoms. As most studies address these constructs, independently from other symptoms and potential risk and protective factors, more insight into the complex relationships among these constructs is needed. This study used the multivariate network approach to gain a better understanding of how patients' symptoms and risk and protective factors (i.e. physical symptoms, social withdrawal, illness cognitions, goal adjustment and partner support) are interconnected. We used cross-sectional data from a sample of cancer patients seeking psychological care (n = 342). Using network modelling, the relationships among symptoms of fatigue, depression and anxiety, and potential risk and protective factors were explored. Additionally, centrality (i.e. the number and strength of connections of a construct) and stability of the network were explored. Among risk factors, the relationship of helplessness and physical symptoms with fatigue stood out as they were stronger than most other connections in the network. Among protective factors, illness acceptance was most centrally embedded within the network, indicating it had more and stronger connections than most other variables in the network. The network identified key connections with risk factors (helplessness, physical symptoms) and a key protective factor (acceptance) at the group level. Longitudinal studies should explore these risk and protective factors in individual dynamic networks to further investigate their causal role and the extent to which such networks can inform us on what treatment would be most suitable for the individual cancer patient.

On Nonregularized Estimation of Psychological Networks

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.

All-optical processes in double quantum dot structure

The ladder-plus-Y double quantum dot structure was modeled for all-optical processing by combining the density matrix theory with the pulse width description of the applied pulse. The momentum matrix elements are calculated including the wetting layer. The ladder-plus-Y structure exhibits pattern-free output with high bit rate (50 Tbps), which is critical in optical communication applications. It is shown that very high ground-state occupation with periodic shape for state occupations is critical in obtaining a pattern-free eye diagram.

Room-temperature implementation of the Deutsch-Jozsa algorithm with a single electronic spin in diamond

The nitrogen-vacancy defect center (N-V center) is a promising candidate for quantum information processing due to the possibility of coherent manipulation of individual spins in the absence of the cryogenic requirement. We report a room-temperature implementation of the Deutsch-Jozsa algorithm by encoding both a qubit and an auxiliary state in the electron spin of a single N-V center. By thus exploiting the specific S=1 character of the spin system, we demonstrate how even scarce quantum resources can be used for test-bed experiments on the way towards a large-scale quantum computing architecture.

Non-Markovian damping of Rabi oscillations in semiconductor quantum dots

A systematic investigation is performed on the damping of Rabi oscillations induced by an external electromagnetic field interacting with a two-level semiconductor system. We have considered a coherently driven two-level system coupled to a dephasing reservoir and shown that, to explain the dependence of the dephasing rate on the driving intensity, it is essential to consider the non-Markovian character of the reservoir. Moreover, we have demonstrated that intensity-dependent damping may be induced by various dephasing mechanisms due to stationary as well as non-stationary effects caused by coupling with the environment. Finally, present results are able to explain a variety of experimental measurements available in the literature.

Deutsch-jozsa algorithm using triggered single photons from a single quantum dot

We demonstrate a two-qubit Deutsch-Jozsa algorithm with single photons from a single InP quantum dot. The qubits are implemented via the spatial mode and the polarization of a single photon. Our photon source is operated both under continuous and pulsed excitation, the latter allowing deterministic quantum logic by generating photons on demand with a strong suppression of two-photon events. The computation reached a success probability of up to 79%. We also exploit the concept of decoherence-free subspaces that helps to make our experimental setup robust against sources of phase noise.

Rabi oscillation damped by exciton leakage and Auger capture in quantum dots

The decoherence of Rabi oscillation (RO) caused by biexciton, population leakage to the wetting layer (WL), and Auger capture in semiconductor quantum dots is theoretically analyzed with multilevel optical Bloch equations. The corresponding effects on the quality factor of RO are also discussed. We have found that the biexciton effect is relatively trifling, as the pulse duration is longer than 5 ps. The population leakage to the WL leads to a decrease of the RO average even though the damping rate is similar to that observed in the experiment. Auger capture in quantum dots results in RO damping that is consistent with the experimental data, which implies that Auger capture is an important decoherence process in quantum dots.

Coherent control of a V-type three-level system in a single quantum dot

In a semiconductor quantum dot, the IIx and IIy transitions to the polarization eigenstates, |x> and |y>, naturally form a three-level V-type system. Using low-temperature polarized photoluminescence spectroscopy, we have investigated the exciton dynamics arising under strong laser excitation. We also explicitly solved the density matrix equations for comparison with the experimental data. The polarization of the exciting field controls the coupling between the otherwise orthogonal states. In particular, when the system is initialized into \Y>, a polarization-tailored pulse can swap the population into |x>, and vice versa, effectively operating on the exciton spin.

Scaling of decoherence in wide NMR quantum registers

Among the most important parameters for the usefulness of quantum computers are the size of the quantum register and the decoherence time for the quantum information. The decoherence time is expected to get shorter with the number of correlated qubits, but experimental data are only available for small numbers of qubits. Solid-state nuclear magnetic resonance allows one to correlate large numbers of qubits (several hundred) and measure their decoherence rates. We use a modified magnetic dipole-dipole interaction to correlate the proton spins in a solid sample and observe the decay of the resulting highly correlated states. By systematically varying the number of correlated spins, we measure the increase of the decoherence rate with the size of the quantum register.