1,354,352 research outputs found
Time-local optimal control for parameter estimation in the Gaussian regime
No full textInformation about a classical parameter encoded in a quantum state can only decrease if the state undergoes a non-unitary evolution, arising from the interaction with an environment. However, instantaneous control unitaries may be used to mitigate the decrease of information caused by an open dynamics. A possible, locally optimal (in time) choice for such controls is the one that maximises the time-derivative of the quantum Fisher information (QFI) associated with a parameter encoded in an initial state. In this study, we focus on a single bosonic mode subject to a Markovian, thermal master equation, and determine analytically the optimal time-local control of the QFI for its initial squeezing angle (optical phase) and strength. We show that a single initial control operation is already optimal for such cases and quantitatively investigate situations where the o
Quality and Impact Monitoring for Local eGovernment Services
No full textPurpose – The purpose of this paper is to introduce a layered, comprehensive model of quality of service (QoS) for local eGovernment, and discuss its feasibility on a regional eGovernment case study. The eGovernment online services are becoming a key infrastructure for advanced countries. They allow significant efficiency gains in different sectors of society, offering benefits for individual citizens and for the community as a whole. The deployment of online services alone is not sufficient in order to qualify an eGovernment strategy. The intrinsic and perceived quality of services offered, as well as the actual impact of new functionalities, should be properly measured and taken into account.
Design/methodology/approach – This paper presents an applied research study for a quality-focused evolution of a service-oriented architecture for local eGovernment portals. This investigation was based on three main layers: the perceived quality and effective impact of services (G2C layer), the effectiveness of the deployed processes (WFM layer) and finally, the system-level efficiency (G2G layer).
Findings – The measurement of quality with respect to eGovernment services is a complex task which requires appropriate tools to tackle the different aspects of the problem. Specifically, active and passive tools (respectively surveys and usage analysis) should be used to evaluate the quality perceived by the users as well as the utility of the service itself. The efficiency of the back office workflow must be estimated measuring statistical and dynamical indicators. Finally, technical measures should be used to monitor the responsiveness and scalability of software implementations and deployment systems.
Social implications – A better knowledge regarding (e-)Government service delivery processes, their QoS and their impact on the society can empower both citizens and local administrators, and can help them to better improve the effectiveness of local government.
Originality/value – The multi-layered quality measurement architecture proposed in this paper offers local governments the capability to systematically monitor and analyse the quality of their online services. The business process management technologies allow citizens to get a better knowledge of the service delivery processes; the QoS measurements allow to improve control on them; and the eGovernment Intelligence model allows to better quantify their actual social impact
Nonlinearity as a resource for nonclassicality in anharmonic systems
No full textNonclassicality is a key ingredient for quantum enhanced technologies and experiments involving macro- scopic quantum coherence. Considering various exactly-solvable quantum-oscillator systems, we address the role played by the anharmonicity of their potential in the establishment of nonclassical features. Specifically, we show that a monotonic relation exists between the the entropic nonlinearity of the considered potentials and their ground state nonclassicality, as quantified by the negativity of the Wigner function. In addition, in order to clarify the role of squeezing--which is not captured by the negativity of the Wigner function--we focus on the Glauber-Sudarshan P-function and address the nonclassicality/nonlinearity relation using the entanglement potential. Finally, we consider the case of a generic sixth-order potential confirming the idea that nonlinearity is a resource for the generation of nonclassicality and may serve as a guideline for the engineering of quantum oscillators
A pedagogical introduction to continuously monitored quantum systems and measurement-based feedback
No full textIn this manuscript we present a pedagogical introduction to continuously monitored quantum systems. We
start by giving a simplified derivation of the Markovian master equation in Lindblad form, in the spirit
of collision models and input-output theory, which describes the unconditional dynamics of a continuously
monitored system. The same formalism is then exploited to derive stochastic master equations that describe the
conditional dynamics. We focus on the two most paradigmatic examples of continuous monitoring: continuous
photodetection, leading to a discontinuous dynamics with “quantum jumps”, and continuous homodyne
measurements, leading to a diffusive dynamics. We then present a derivation of feedback master equations that
describe the dynamics (either conditional or unconditional) when the continuous measurement photocurrents
are fed back to the system as a linear driving Hamiltonian, a paradigm known as linear Markovian feedback.
In the second part of the manuscript we focus on continuous-variable Gaussian systems: we first present the
equations for first and second moments describing the dynamics under continuous general-dyne measurements,
and we then discuss in more detail the conditional and unconditional dynamics under Markovian and state-based
feedback
A Survey on Text Classification Algorithms: From Text to Predictions
No full textIn recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models
Locally optimal control of continuous-variable entanglement
No full textWe consider a system of two bosonic modes each subject to the dynamics induced by a thermal Markovian environment and we identify instantaneous, local symplectic controls that minimize the loss of entanglement in the Gaussian regime. By minimizing the decrease of the logarithmic negativity at every instant in time, it will be shown that a nontrivial, finite amount of local squeezing helps to counter the effect of decoherence during the evolution. We also determine optimal control routines in the more restrictive scenario where the control operations are applied on only one of the two modes. We find that applying an instantaneous control only at the beginning of the dynamics, i.e., preparing an appropriate initial state, is the optimal strategy for states with symmetric correlations and when the dynamics is the same on both modes. More generally, even in asymmetric cases, the delayed decay of entanglement resulting from the optimal preparation of the initial state with no further action turns out to be always very close to the optimized control where multiple operations are applied during the evolution. Our study extends directly to “monosymmetric” systems of any number of modes, i.e., to systems that are invariant under any local permutation of the modes within any one partition, as they are locally equivalent to two-mode systems
Consistent Partial Matching of Shape Collections via Sparse Modeling
No full textRecent efforts in the area of joint object matching approach the problem by taking as input a set of pairwise maps, which are then jointly optimized across the whole collection so that certain accuracy and consistency criteria are satisfied. One natural requirement is cycle-consistencynamely the fact that map composition should give the same result regardless of the path taken in the shape collection. In this paper, we introduce a novel approach to obtain consistent matches without requiring initial pairwise solutions to be given as input. We do so by optimizing a joint measure of metric distortion directly over the space of cycle-consistent maps; in order to allow for partially similar and extra-class shapes, we formulate the problem as a series of quadratic programs with sparsity-inducing constraints, making our technique a natural candidate for analysing collections with a large presence of outliers. The particular form of the problem allows us to leverage results and tools from the field of evolutionary game theory. This enables a highly efficient optimization procedure which assures accurate and provably consistent solutions in a matter of minutes in collections with hundreds of shapes
Enhanced estimation of loss in the presence of Kerr nonlinearity
No full textWe address the characterization of dissipative bosonic channels and show that estimation of the loss rate by Gaussian probes (coherent or squeezed) is improved in the presence of Kerr nonlinearity. In particular, enhancement of precision may be substantial for short interaction time, i.e., for media of moderate size, e.g., biological samples. We analyze in detail the behavior of the quantum Fisher information (QFI), and determine the values of nonlinearity maximizing the QFI as a function of the interaction time and of the parameters of the input signal. We also discuss the precision achievable by photon counting and quadrature measurement and present additional results for truncated, few-photon, probe signals. Finally, we discuss the origin of the precision enhancement, showing that it cannot be linked quantitatively to the non-Gaussianity or the nonclassicality of the interacting probe signal
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