Quantum Control Landscapes

Numerous lines of experimental, numerical and analytical evidence indicate that it is surprisingly easy to locate optimal controls steering quantum dynamical systems to desired objectives. This has enabled the control of complex quantum systems despite the expense of solving the Schrodinger equation in simulations and the complicating effects of environmental decoherence in the laboratory. Recent work indicates that this simplicity originates in universal properties of the solution sets to quantum control problems that are fundamentally different from their classical counterparts. Here, we review studies that aim to systematically characterize these properties, enabling the classification of quantum control mechanisms and the design of globally efficient quantum control algorithms.Comment: 45 pages, 15 figures; International Reviews in Physical Chemistry, Vol. 26, Iss. 4, pp. 671-735 (2007

Optimization search effort over the control landscapes for open quantum systems with Kraus-map evolution

A quantum control landscape is defined as the expectation value of a target observable $\Theta$ as a function of the control variables. In this work control landscapes for open quantum systems governed by Kraus map evolution are analyzed. Kraus maps are used as the controls transforming an initial density matrix $\rho_{\rm i}$ into a final density matrix to maximize the expectation value of the observable $\Theta$. The absence of suboptimal local maxima for the relevant control landscapes is numerically illustrated. The dependence of the optimization search effort is analyzed in terms of the dimension of the system $N$, the initial state $\rho_{\rm i}$, and the target observable $\Theta$. It is found that if the number of nonzero eigenvalues in $\rho_{\rm i}$ remains constant, the search effort does not exhibit any significant dependence on $N$. If $\rho_{\rm i}$ has no zero eigenvalues, then the computational complexity and the required search effort rise with $N$. The dimension of the top manifold (i.e., the set of Kraus operators that maximizes the objective) is found to positively correlate with the optimization search efficiency. Under the assumption of full controllability, incoherent control modelled by Kraus maps is found to be more efficient in reaching the same value of the objective than coherent control modelled by unitary maps. Numerical simulations are also performed for control landscapes with linear constraints on the available Kraus maps, and suboptimal maxima are not revealed for these landscapes.Comment: 29 pages, 8 figure

Puccinia gladioli

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Positron Emission Tomography in Heart Failure: From Pathophysiology to Clinical Application.

Imaging modalities are increasingly being used to evaluate the underlying pathophysiology of heart failure. Positron emission tomography (PET) is a non-invasive imaging technique that uses radioactive tracers to visualize and measure biological processes in vivo. PET imaging of the heart uses different radiopharmaceuticals to provide information on myocardial metabolism, perfusion, inflammation, fibrosis, and sympathetic nervous system activity, which are all important contributors to the development and progression of heart failure. This narrative review provides an overview of the use of PET imaging in heart failure, highlighting the different PET tracers and modalities, and discussing fields of present and future clinical application

Valence-Dependent Coupling of Prefrontal-Amygdala Effective Connectivity during Facial Affect Processing

Despite the importance of the prefrontal-amygdala (AMY) network for emotion processing, valence-dependent coupling within this network remains elusive. In this study, we assessed the effect of emotional valence on brain activity and effective connectivity. We tested which functional pathways within the prefrontal-AMY network are specifically engaged during the processing of emotional valence. Thirty-three healthy adults were examined with functional magnetic resonance imaging while performing a dynamic faces and dynamic shapes matching task. The valence of the facial expressions varied systematically between positive, negative, and neutral across the task. Functional contrasts determined core areas of the emotion processing circuitry, comprising the medial prefrontal cortex (MPFC), the right lateral prefrontal cortex (LPFC), the AMY, and the right fusiform face area (FFA). Dynamic causal modelling demonstrated that the bidirectional coupling within the prefrontal-AMY circuitry is modulated by emotional valence. Additionally, Bayesian model averaging showed significant bottom-up connectivity from the AMY to the MPFC during negative and neutral, but not positive, valence. Thus, our study provides strong evidence for alterations of bottom-up coupling within the prefrontal-AMY network as a function of emotional valence. Thereby our results not only advance the understanding of the human prefrontal-AMY circuitry in varying valence context, but, moreover, provide a model to examine mechanisms of valence-sensitive emotional dysregulation in neuropsychiatric disorders

Valence-Dependent Coupling of Prefrontal-Amygdala Effective Connectivity during Facial Affect Processing

Despite the importance of the prefrontal-amygdala (AMY) network for emotion processing, valence-dependent coupling within this network remains elusive. In this study, we assessed the effect of emotional valence on brain activity and effective connectivity. We tested which functional pathways within the prefrontal-AMY network are specifically engaged during the processing of emotional valence. Thirty-three healthy adults were examined with functional magnetic resonance imaging while performing a dynamic faces and dynamic shapes matching task. The valence of the facial expressions varied systematically between positive, negative, and neutral across the task. Functional contrasts determined core areas of the emotion processing circuitry, comprising the medial prefrontal cortex (MPFC), the right lateral prefrontal cortex (LPFC), the AMY, and the right fusiform face area (FFA). Dynamic causal modelling demonstrated that the bidirectional coupling within the prefrontal-AMY circuitry is modulated by emotional valence. Additionally, Bayesian model averaging showed significant bottom-up connectivity from the AMY to the MPFC during negative and neutral, but not positive, valence. Thus, our study provides strong evidence for alterations of bottom-up coupling within the prefrontal-AMY network as a function of emotional valence. Thereby our results not only advance the understanding of the human prefrontal-AMY circuitry in varying valence context, but, moreover, provide a model to examine mechanisms of valence-sensitive emotional dysregulation in neuropsychiatric disorders