Quantum computing by optical control of electron spins

We review the progress and main challenges in implementing large-scale quantum computing by optical control of electron spins in quantum dots (QDs). Relevant systems include self-assembled QDs of III-V or II-VI compound semiconductors (such as InGaAs and CdSe), monolayer fluctuation QDs in compound semiconductor quantum wells, and impurity centres in solids, such as P-donors in silicon and nitrogen-vacancy centres in diamond. The decoherence of the electron spin qubits is discussed and various schemes for countering the decoherence problem are reviewed. We put forward designs of local nodes consisting of a few qubits which can be individually addressed and controlled. Remotely separated local nodes are connected by photonic structures (microcavities and waveguides) to form a large-scale distributed quantum system or a quantum network. The operation of the quantum network consists of optical control of a single electron spin, coupling of two spins in a local nodes, optically controlled quantum interfacing between stationary spin qubits in QDs and flying photon qubits in waveguides, rapid initialization of spin qubits and qubit-specific single-shot non-demolition quantum measurement. The rapid qubit initialization may be realized by selectively enhancing certain entropy dumping channels via phonon or photon baths. The single-shot quantum measurement may be in situ implemented through the integrated photonic network. The relevance of quantum non-demolition measurement to large-scale quantum computation is discussed. To illustrate the feasibility and demand, the resources are estimated for the benchmark problem of factorizing 15 with Shor's algorithm. © 2010 Taylor & Francis.postprin

Persistent optical nuclear spin narrowing in a singly charged InAs quantum dot

We review the investigation of the hole-assisted dynamical nuclear spin polarization mechanism in a singly charged InAs quantum dot. Using coherent dark state spectroscopy, we measure the locking of the Overhauser field to a value determined only by the laser frequencies. Importantly, we review data that the locking effect can suppress nuclear spin fluctuations. We determine the onset time of the nuclear spin narrowing effect and its persistence absent laser interactions by directly measuring the enhancement of the electron spin coherence. This nuclear field locking effect can be explained in terms of an anisotropic hyperfine coupling between the hole spin and the nuclear spins. © 2012 Optical Society of America.published_or_final_versio

Multi-seeded melt growth (MSMG) of bulk Y-Ba-Cu-O using thin-film seeds

Y-Ba-Cu-O (YBCO) and Sm-Ba-Cu-O (SmBCO) thin films have been used for the first time as heterogeneous seeds to multi-seed successfully the melt growth of bulk YBCO in a multi-seeded melt growth (MSMG) process. The use of thin film seeds, which may be prepared with highly controlled orientation (i.e. with a well-defined a-b plane and precisely known a-direction), is based on their superheating properties and reduces significantly contamination of the bulk sample by the seed material. A variety of grain boundaries were obtained by varying the angle between the seeds. Microstructural studies indicate that the extent of residual melt deposited at the grain boundary decreases with increasing grain boundary contact angle. It is established that the growth front proceeds continuously at the (110)/(110) grain boundary without trapping liquid, which leads to the formation of a clean grain boundary

Sub-Sets of Cancer Stem Cells Differ Intrinsically in Their Patterns of Oxygen Metabolism

PMCID: PMC3640080This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

In-situ observations of flux ropes formed in association with a pair of spiral nulls in magnetotail plasmas

Signatures of secondary islands are frequently observed in the magnetic reconnection regions of magnetotail plasmas. In this paper, magnetic structures with the secondary-island signatures observed by Cluster are reassembled by a fitting-reconstruction method. The results show three-dimensionally that a secondary island event can manifest the flux rope formed with an As-type null and a Bs-type null paired via their spines. We call this As-spine-Bs-like configuration the helically wrapped spine model. The reconstructed field lines wrap around the spine to form the flux rope, and an O-type topology is therefore seen on the plane perpendicular to the spine. Magnetized electrons are found to rotate on and cross the fan surface, suggesting that both the torsional-spine and the spine-fan reconnection take place in the configuration. Furthermore, detailed analysis implies that the spiral nulls and flux ropes were locally generated nearby the spacecraft in the reconnection outflow region, indicating that secondary reconnection may occur in the exhaust away from the primary reconnection site

Benchmark performance of low-cost Sb2Se3 photocathodes for unassisted solar overall water splitting

Determining cost-effective semiconductors exhibiting desirable properties for commercial photoelectrochemical water splitting remains a challenge. Herein, we report a Sb2Se3 semiconductor that satisfies most requirements for an ideal high-performance photoelectrode, including a small band gap and favourable cost, optoelectronic properties, processability, and photocorrosion stability. Strong anisotropy, a major issue for Sb2Se3, is resolved by suppressing growth kinetics via close space sublimation to obtain high-quality compact thin films with favourable crystallographic orientation. The Sb2Se3 photocathode exhibits a high photocurrent density of almost 30mAcm(-2) at 0V against the reversible hydrogen electrode, the highest value so far. We demonstrate unassisted solar overall water splitting by combining the optimised Sb2Se3 photocathode with a BiVO4 photoanode, achieving a solar-to-hydrogen efficiency of 1.5% with stability over 10h under simulated 1 sun conditions employing a broad range of solar fluxes. Low-cost Sb2Se3 can thus be an attractive breakthrough material for commercial solar fuel production. While photoelectrochemical water splitting offers an integrated means to convert sunlight to a renewable fuel, cost-effective light-absorbers are rare. Here, authors report Sb2Se3 photocathodes for high-performance photoelectrochemical water splitting devices

The Influence of Direct and Indirect Speech on Mental Representations

Language can be viewed as a set of cues that modulate the comprehender's thought processes. It is a very subtle instrument. For example, the literature suggests that people perceive direct speech (e.g., Joanne said: 'I went out for dinner last night') as more vivid and perceptually engaging than indirect speech (e.g., Joanne said that she went out for dinner last night). But how is this alleged vividness evident in comprehenders' mental representations? We sought to address this question in a series of experiments. Our results do not support the idea that, compared to indirect speech, direct speech enhances the accessibility of information from the communicative or the referential situation during comprehension. Neither do our results support the idea that the hypothesized more vivid experience of direct speech is caused by a switch from the visual to the auditory modality. However, our results do show that direct speech leads to a stronger mental representation of the exact wording of a sentence than does indirect speech. These results show that language has a more subtle influence on memory representations than was previously suggested

Neutron Majorana mass from exotic instantons

We show how a Majorana mass for the Neutron could result from non-perturbative quantum gravity effects peculiar to string theory. In particular, "exotic instantons" in un-oriented string compactifications with D-branes extending the (supersymmetric) standard model could indirectly produce an effective operator delta{m} n^t n+h.c. In a specific model with an extra vector-like pair of `quarks', acquiring a large mass proportional to the string mass scale (exponentially suppressed by a function of the string moduli fields), delta{m} can turn out to be as low as 10^{-24}-10^{-25} eV. The induced neutron-antineutron oscillations could take place with a time scale tau_{n\bar{n}} > 10^8 s, that could be tested by the next generation of experiments. On the other hand, proton decay and FCNC's are automatically strongly suppressed and are compatible with the current experimental limits. Depending on the number of brane intersections, the model may also lead to the generation of Majorana masses for R-handed neutrini. Our proposal could also suggest neutron-neutralino or neutron-axino oscillations, with implications in UCN, Dark Matter Direct Detection, UHECR and Neutron-Antineutron oscillations. This suggests to improve the limits on neutron-antineutron oscillations, as a possible test of string theory and quantum gravity.Comment: 35 pages, 11 figures. More comments on neutron-neutralino mixin

A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease

Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.Centro de Investigación Biomédica en RedDepto. de Psicología Experimental, Procesos Cognitivos y LogopediaDepto. de Radiología, Rehabilitación y FisioterapiaFac. de PsicologíaFac. de MedicinaTRUEpu

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques