Qubits based on colour centres in diamond became a prominent system for solid-state quantum information processing and sensing. But the deterministic creation of qubits and the control of their environment are still critical issues, preventing the development of a room-temperature quantum computer. We report on the high creation yield of NV centres of 75% (a tenfold enhancement) by charge-assisted defect engineering, together with an improvement of their spin coherence. The method strongly favours the formation and negative charge state of the NV centres with respect to intrinsic diamond, while it hinders the formation of competing and perturbing defects such as di-vacancies or NVH complexes. We evidence spectrally the charge state tuning of the implantation-induced vacancies from V0 to V−, key element of this Coulomb-driven engineering. The generality of the method is demonstrated using several donors (phosphorous, oxygen and sulphur) and applying it to other centres (SnV and MgV) in diamond.

The magnetization of nitrogen-doped single crystalline diamond bulk samples shows unconventional field and temperature hysteresis loops at T 25 K. The results suggest the existence of superparamagnetic and superconducting regions in samples with nitrogen concentration <200 ppm. Both phases vanish at temperatures above 25 K where the samples show diamagnetic behavior similar to undoped diamond. The observation of superparamagnetism and superconductivity is attributed to the nitrogen doping and to the existence of defective regions. From particle-induced X-ray emission with ppm resolution we rule out that the main observations below 25 K are due to magnetic impurities. We investigated also the magnetic properties of ferromagnetic/high-temperature superconducting oxide bilayers. The magnetization results obtained from those bilayers show remarkable similarities to the ones in nitrogen-doped diamond.

In this paper, we demonstrate an active control of the charge state of a single nitrogen-vacancy (NV) centre by using in-plane Schottky-diode geometries with aluminium on hydrogen-terminated diamond surface. A switching between NV+, NV0 and NV− can be performed with the Al-gates which apply electric fields in the hole depletion region of the Schottky junction that induces a band bending modulation, thereby shifting the Fermi-level over NV charge transition levels. We simulated the in-plane band structure of the Schottky junction with the Software ATLAS by solving the drift-diffusion model and the Poisson-equation self-consistently. We simulated the IV-characteristics, calculated the width of the hole depletion region, the position of the Fermi-level intersection with the NV charge transition levels for different reverse bias voltages applied on the Al-gate. We can show that the field-induced band bending modulation in the depletion region causes a shifting of the Fermi-level over NV charge transition levels in such a way that the charge state of a single NV centre and thus its electrical and optical properties is tuned. In addition, the NV centre should be approx. 1–2 μm away from the Al-edge in order to be switched with moderate bias voltages.

This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we propose a policy improvement method to obtain computable approximations of the optimal strategy. Numerical results are presented at the end.

This paper investigates optimal trading strategies in a financial market with multidimensional stock returns where the drift is an unobservable multivariate Ornstein-Uhlenbeck process. Information about the drift is obtained by observing stock returns and expert opinions. The latter provide unbiased estimates on the current state of the drift at discrete points in time.The optimal trading strategy of investors maximizing expected logarithmic utility of terminal wealth depends on the filter which is the conditional expectation of the drift given the available information. We state filtering equations to describe its dynamics for different information settings. Between expert opinions this is the Kalman filter. The conditional covariance matrices of the filter follow ordinary differential equations of Riccati type. We rely on basic theory about matrix Riccati equations to investigate their properties. Firstly, we consider the asymptotic behaviour of the covariance matrices for an increasing number of expert opinions on a finite time horizon. Secondly, we state conditions for the convergence of the covariance matrices on an infinite time horizon with regularly arriving expert opinions.Finally, we derive the optimal trading strategy of an investor. The optimal expected logarithmic utility of terminal wealth, the value function, is a functional of the conditional covariance matrices. Hence, our analysis of the covariance matrices allows us to deduce properties of the value function.

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Abstract. This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased estimate of the current state of the drift. Nevertheless, the drift can only be observed partially and the best estimate is given by the conditional expectation given the available information, i.e., by the filter. We provide the filter equations in the model with expert opinion and derive in detail properties of the conditional variance. For an investor who maximizes expected logarithmic utility of his portfolio, we derive the optimal strategy explicitly in different settings for the available information. The optimal expected utility, the value function of the control problem, depends on the conditional variance. The bounds and asymptotic results for the conditional variances are used to derive bounds and asymptotic properties for the value functions. The results are illustrated with numerical examples.

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