Highly Non-linear Excitonic Zeeman Spin-Splitting in Composition-Engineered Artificial Atoms

Non-linear Zeeman splitting of neutral excitons is observed in composition engineered In(x)Ga(1-x)As self-assembled quantum dots and its microscopic origin is explained. Eight-band k.p simulations, performed using realistic dot parameters extracted from cross-sectional scanning tunneling microscopy, reveal that a quadratic contribution to the Zeeman energy originates from a spin dependent mixing of heavy and light hole orbital states in the dot. The dilute In-composition (x<0.35) and large lateral size (40-50 nm) of the quantum dots investigated is shown to strongly enhance the non-linear excitonic Zeeman gap, providing a blueprint to enhance such magnetic non-linearities via growth engineering

Critical voltage of a mesoscopic superconductor

We study the role of the quasiparticle distribution function f on the properties of a superconducting nanowire. We employ a numerical calculation based upon the Usadel equation. Going beyond linear response, we find a non-thermal distribution for f caused by an applied bias voltage. We demonstrate that the even part of f (the energy mode f_L) drives a first order transition from the superconducting state to the normal state irrespective of the current

Benchmarking high fidelity single-shot readout of semiconductor qubits

Determination of qubit initialisation and measurement fidelity is important for the overall performance of a quantum computer. However, the method by which it is calculated in semiconductor qubits varies between experiments. In this paper we present a full theoretical analysis of electronic single-shot readout and describe critical parameters to achieve high fidelity readout. In particular, we derive a model for energy selective state readout based on a charge detector response and examine how to optimise the fidelity by choosing correct experimental parameters. Although we focus on single electron spin readout, the theory presented can be applied to other electronic readout techniques in semiconductors that use a reservoir.Comment: 19 pages, 8 figure

Efficient Reactive Brownian Dynamics

We develop a Split Reactive Brownian Dynamics (SRBD) algorithm for particle simulations of reaction-diffusion systems based on the Doi or volume reactivity model, in which pairs of particles react with a specified Poisson rate if they are closer than a chosen reactive distance. In our Doi model, we ensure that the microscopic reaction rules for various association and disassociation reactions are consistent with detailed balance (time reversibility) at thermodynamic equilibrium. The SRBD algorithm uses Strang splitting in time to separate reaction and diffusion, and solves both the diffusion-only and reaction-only subproblems exactly, even at high packing densities. To efficiently process reactions without uncontrolled approximations, SRBD employs an event-driven algorithm that processes reactions in a time-ordered sequence over the duration of the time step. A grid of cells with size larger than all of the reactive distances is used to schedule and process the reactions, but unlike traditional grid-based methods such as Reaction-Diffusion Master Equation (RDME) algorithms, the results of SRBD are statistically independent of the size of the grid used to accelerate the processing of reactions. We use the SRBD algorithm to compute the effective macroscopic reaction rate for both reaction- and diffusion-limited irreversible association in three dimensions. We also study long-time tails in the time correlation functions for reversible association at thermodynamic equilibrium. Finally, we compare different particle and continuum methods on a model exhibiting a Turing-like instability and pattern formation. We find that for models in which particles diffuse off lattice, such as the Doi model, reactions lead to a spurious enhancement of the effective diffusion coefficients.Comment: To appear in J. Chem. Phy

Composition profiling InAs quantum dots and wetting layers by atom probe tomography and cross-sectional scanning tunnelling microscopy

This study compares cross-sectional scanning tunnelling microscopy (XSTM) and atom probe tomography (APT). We use epitaxially grown self-assembled InAs quantum dots (QDs) in GaAs as an exemplary material with which to compare these two nanostructural analysis techniques. We studied the composition of the wetting layer and the QDs, and performed quantitative comparisons of the indium concentration profiles measured by each method. We show that computational models of the wetting layer and the QDs, based on experimental data, are consistent with both analytical approaches. This establishes a link between the two techniques and shows their complimentary behaviour, an advantage which we exploit in order to highlight unique features of the examined QD material.Comment: Main article: 8 pages, 6 figures. Appendix: 3 pages, 5 figure

Strong electrically tunable exciton g-factors in an individual quantum dots due to hole orbital angular momentum quenching

Strong electrically tunable exciton g-factors are observed in individual (Ga)InAs self-assembled quantum dots and the microscopic origin of the effect is explained. Realistic eight band k.p simulations quantitatively account for our observations, simultaneously reproducing the exciton transition energy, DC Stark shift, diamagnetic shift and g-factor tunability for model dots with the measured size and a comparatively low In-composition of x(In)~35% near the dot apex. We show that the observed g-factor tunability is dominated by the hole, the electron contributing only weakly. The electric field induced perturbation of the hole wavefunction is shown to impact upon the g-factor via orbital angular momentum quenching, the change of the In:Ga composition inside the envelope function playing only a minor role. Our results provide design rules for growing self-assembled quantum dots for electrical spin manipulation via electrical g-factor modulation

FLOCK: Fast, Lightweight, and Scalable Allocation for Decentralized Services on Blockchain

Many decentralized services have recently emerged on top of blockchain, offering benefits like privacy, and allowing any node in the network to share its resources. In order to be a competitive alternative to their central counterparts, their performance needs to match up. Specifically, service allocation remains a performance bottleneck for many decentralized services.In this paper we present FLOCK, an allocation system which is highly scalable, fast, and lightweight. Furthermore, it allows nodes to indicate their preference for clients/sellers without needing to submit bids by using stable matching algorithms. We decouple the price discovery and outsource this function to a smart contract on the blockchain.Additionally, another smart contract is used to orchestrate the allocation and take care of service discovery, while trusted execution environments securely compute allocation solutions, and off-chain payment networks are used to send rewards.Evaluation of FLOCK shows that gas costs are manageable and improve upon other solutions which leverage auctions, and that our instance of the stable matching algorithm greatly improves run-time and throughput over auction counterparts. Finally, our discussion outlines practical improvements to further increase performance

The Case for AI Based Web3 Reputation Systems

Initiatives such as blockchains and decentralized storage networks are pushing for a decentralized Web3 to replace the current architecture. At the core of Web3 are network resource sharing services, which allow anyone to sell spare network capacity in return for rewards. These services require a way to establish trust, as parties are potentially malicious. This can be achieved by reputation systems. In this paper we make the case for using deep reinforcement learning in Web3 reputation calculation. More specifically, we propose a model which allows for decentralized calculation of scores with high personalization for the user