Charge dynamics in two-electron quantum dots

We investigate charge dynamics in a two-electron double quantum dot. The quantum dot is manipulated by using a time-dependent external voltage that induces charge oscillations between the dots. We study the dependence of the charge dynamics on the external magnetic field and on the periodicity of the external potential. We find that for suitable parameter values, it is possible to induce both one-electron and two-electron oscillations between the dots.Comment: 4 pages, 7 figures, proceedings of the Quantum Dot 2010 conferenc

Single Qubit State Estimation on NISQ Devices with Limited Resources and SIC-POVMs

Current quantum computers have the potential to overcome classical computational methods, however, the capability of the algorithms that can be executed on noisy intermediate-scale quantum devices is limited due to hardware imperfections. Estimating the state of a qubit is often needed in different quantum protocols, due to the lack of direct measurements. In this paper, we consider the problem of estimating the quantum state of a qubit in a quantum processing unit without conducting direct measurements of it. We consider a parameterized measurement model to estimate the quantum state, represented as a quantum circuit, which is optimized using the quantum tomographic transfer function. We implement and test the circuit using the quantum computer of the Technical Research Centre of Finland as well as an IBM quantum computer. We demonstrate that the set of positive operator-valued measurements used for the estimation is symmetric and informationally complete. Moreover, the resources needed for qubit estimation are reduced when direct measurements are allowed, keeping the symmetric property of the measurements.Comment: Conference paper for the IEEE International Conference on Quantum Computing and Engineering (QCE) 202

Investigation of municipal solid waste (MSW) and industrial landfills as a potential source of secondary raw materials

Many of the secondary raw materials (SRM) in landfills constitute valuable and scarce natural resources. It has already been recognised that the recovery of these elements is critical for the sustainability of a number of industries and SRM recov¬ery from anthropogenic waste deposits represents a significant opportunity. In this study, the characterisation of the different waste fractions and the amount of SRM that can potentially be recovered from two landfill sites in Finland is presented. The first site was a municipal solid waste (MSW) landfill site and it was specifically in¬vestigated for its metals, SRM, plastics, wood, paper, and cardboard content as well as its fine fraction (<20 mm). The second site was an industrial landfill site contain¬ing residual wastes from industrial processes including 1) aluminium salt slag from refining process of aluminium scrap and 2) shredding residues from automobiles, household appliances and other metals containing waste. This site was investigated for its metals and SRM recovery potential as well as its fine fraction. Results suggest that the fine fraction offers opportunities for metal (Cr, Cu, Ni, Pb, and Zn) and SRM extraction and recovery from both landfill site types while the chemical composition of the industrial waste landfill offered greater opporutinity as it was comparable to typical aluminium salt slags. Nevertheless, the concentrations of rare earth metals (REE) and other valuable elements were low even in comparison with the concentra¬tions found in the Earth’s crust. Therefore mining landfill sites only for their metals or SRM content is not expected to be financially viable. However, other opportunities, such as waste-derived fuels from excavated materials especially at MSW landfill sites, still exists and fosters the application and feasibility of landfill mining

Enhancement by postfiltering for speech and audio coding in ad-hoc sensor networks

Enhancement algorithms for wireless acoustics sensor networks~(WASNs) are indispensable with the increasing availability and usage of connected devices with microphones. Conventional spatial filtering approaches for enhancement in WASNs approximate quantization noise with an additive Gaussian distribution, which limits performance due to the non-linear nature of quantization noise at lower bitrates. In this work, we propose a postfilter for enhancement based on Bayesian statistics to obtain a multidevice signal estimate, which explicitly models the quantization noise. Our experiments using PSNR, PESQ and MUSHRA scores demonstrate that the proposed postfilter can be used to enhance signal quality in ad-hoc sensor networks

Coherent control of three-spin states in a triple quantum dot

Spin qubits involving individual spins in single quantum dots or coupled spins in double quantum dots have emerged as potential building blocks for quantum information processing applications. It has been suggested that triple quantum dots may provide additional tools and functionalities. These include the encoding of information to either obtain protection from decoherence or to permit all-electrical operation, efficient spin busing across a quantum circuit, and to enable quantum error correction utilizing the three-spin Greenberger-Horn-Zeilinger quantum state. Towards these goals we demonstrate for the first time coherent manipulation between two interacting three-spin states. We employ the Landau-Zener-St\"uckelberg approach for creating and manipulating coherent superpositions of quantum states. We confirm that we are able to maintain coherence when decreasing the exchange coupling of one spin with another while simultaneously increasing its coupling with the third. Such control of pairwise exchange is a requirement of most spin qubit architectures but has not been previously demonstrated.Comment: 12 pages, 13 figures, and 2 table

An advanced Bayesian model for the visual tracking of multiple interacting objects

Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach

Assessing the opportunities of landfill mining as a source of critical raw materials in Europe

Many of the metals in landfill constitute valuable and scarce natural resources. It has already been recognised that the recovery of these elements is critical for the sustainability of a number of industries. Arsenic (which is an essential part of the production of transistors and LEDs) is predicted to run out sometime in the next five to 50 years if consumption continues at the present rate. Nickel used for anything involving stainless steel and platinum group metals (PGMs) used in catalytic converters, fertilisers and others are also identified as critical materials (CM) to the EU economy at risk of depletion However, despite the increasing demand, none of this supply is supported by recycling. This is due to the high cost of recovery from low concentrations when compared to conventional mining. As demonstrated by the two pilot case studies of this study, mining landfill sites only for their metals content is not expected to be financially viable. However, other opportunities such as Waste-derived fuels from excavated materials exist which if combined , form the concept of ‘enhanced landfill mining’. have the potential to be highly energetic. The energy potential is comparable to the levels of energy of Refuse-Derived Fuels (RDF) produced from non-landfilled wastes

ExoMars 2016 Schiaparelli Module Trajectory and Atmospheric Profiles Reconstruction: Analysis of the On-board Inertial and Radar Measurements

On 19th October 2016 Schiaparelli module of the ExoMars 2016 mission flew through the Mars atmosphere. After successful entry and descent under parachute, the module failed the last part of the descent and crashed on the Mars surface. Nevertheless the data transmitted in real time by Schiaparelli during the entry and descent, together with the entry state vector as initial condition, have been used to reconstruct both the trajectory and the profiles of atmospheric density, pressure and temperature along the traversed path. The available data-set is only a small sub-set of the whole data acquired by Schiaparelli, with a limited data rate (8 kbps) and a large gap during the entry because of the plasma blackout on the communications. This paper presents the work done by the AMELIA (Atmospheric Mars Entry and Landing Investigations and Analysis) team in the exploitation of the available inertial and radar data. First a reference trajectory is derived by direct integration of the inertial measurements and a strategy to overcome the entry data gap is proposed. First-order covariance analysis is used to estimate the uncertainties on all the derived parameters. Then a refined trajectory is computed incorporating the measurements provided by the on-board radar altimeter. The derived trajectory is consistent with the events reported in the telemetry and also with the impact point identified on the high-resolution images of the landing site. Finally, atmospheric profiles are computed tacking into account the aerodynamic properties of the module. Derived profiles result in good agreement with both atmospheric models and available remote sensing observations

Quantifying uncertainty, variability and likelihood for ordinary differential equation models

<p>Abstract</p> <p>Background</p> <p>In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space.</p> <p>Results</p> <p>The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability.</p> <p>Conclusions</p> <p>While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.</p