Efficient numerical approach to high-fidelity phase-modulated gates in long chains of trapped ions.

Almost every quantum circuit is built with two-qubit gates in the current stage, which are crucial to the quantum computing in any platform. The entangling gates based on Mølmer-Sørensen schemes are widely exploited in the trapped-ion system, with the utilization of the collective motional modes of ions and two laser-controlled internal states, which are served as qubits. The key to realize high-fidelity and robust gates is the minimization of the entanglement between the qubits and the motional modes under various sources of errors after the gate operation. In this work, we propose an efficient numerical method to search high-quality solutions for phase-modulated pulses. Instead of directly optimizing a cost function, which contains the fidelity and the robustness of the gates, we convert the problem to the combination of linear algebra and the solution to quadratic equations. Once a solution with the gate fidelity of 1 is found, the laser power can be further reduced while searching on the manifold where the fidelity remains 1. Our method largely overcomes the problem of the convergence and is shown to be effective up to 60 ions, which suffices the need of the gate design in current trapped-ion experiments.

Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data.

OBJECTIVE: Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucial to treatment process. However, due to depression's comorbid nature and the subjectivity in diagnosis, an early diagnosis could be challenging. Recently, machine learning approaches have been used to process Electroencephalography (EEG) and neuroimaging data to facilitate the diagnosis. In the present study, we used a multimodal machine learning approach involving EEG, eye tracking and galvanic skin response data as input to classify depression patients and healthy controls. METHODS: One hundred and forty-four MDD depression patients and 204 matched healthy controls were recruited. They were required to watch a series of affective and neutral stimuli while EEG, eye tracking information and galvanic skin response were recorded via a set of low-cost, portable devices. Three machine learning algorithms including Random Forests, Logistic Regression and Support Vector Machine (SVM) were trained to build dichotomous classification model. RESULTS: The results showed that the highest classification f1 score was obtained by Logistic Regression algorithms, with accuracy = 79.63%, precision = 76.67%, recall = 85.19% and f1 score = 80.70% LIMITATIONS: No hospitalized patients were available; only outpatients were included in the present study. The sample consisted mostly of young adult, and no elder patients were included. CONCLUSIONS: The machine learning approach can be a useful tool for classifying MDD patients and healthy controls and may help for diagnostic processes.

Longitudinal informed consent competency in stable community patients with schizophrenia: A one-week training and one-year follow-up study.

OBJECTIVE: This study explores the efficacy of a one-week informed consent information training process for improving competence to provide consent in stable community patients with schizophrenia over a one-year follow-up period. METHOD: A one-week training session designed to enhance competence to provide informed consent for research was conducted. The training was guided by a research protocol that provided detailed explanations of each major conceptual unit. Participants were followed for one year, and comprised 50 stable community outpatients with schizophrenia who were randomly assigned to either an intervention group (IG, n=33) or a control group (CG, n=17) that did not receive any training. The Chinese Mandarin MacArthur Competence Assessment Tool-Clinical Research (MacCAT-CR) was used to assess competence for informed consent pre-training (baseline), post-training (one week later), and at the end of one year. RESULTS: A repeated measures analysis comparing the IG and CG at pre-training, post-training and the one-year follow-up revealed significant improvements in the Understanding and Appreciation subscale following the one-week training. However, by the end of one year, scores returned to baseline levels, with the exception of the Appreciation subscale, which was below baseline. There were significant main effects across time on the Appreciation and Reasoning subscales, indicating considerable changes over time. The CG showed no significant changes from pre-training to one week later or at one-year follow-up for all subscales. CONCLUSIONS: The capacities of understanding and appreciation can be improved in stable community patients with schizophrenia after one week of training; however, this training effect is not sustained one year later. The results suggest that more intensive or periodic trainings may be needed to maintain long-term competence levels in patients with schizophrenia, particularly with regard to their capacity to appreciate the nature and consequences of study participation.

[An outbreak of foot pain syndrome among students from a senior high school in Foshan, Guangdong province, 2014].

OBJECTIVE: To identify the cause of an outbreak of foot pain syndrome among students from a senior high school in Foshan. METHODS: We defined a suspect case as onset of foot pain/numbness with unknown reason among students and teachers in a school of Foshan city, from February 10 to March 16, 2014. A suspect case was noticed as having both food pain and numbness. All the cases were searched through reviewing medical records in the nearby hospitals and school's clinic, also the records of absenteeism in school. Clinical information was collected from all the students, using a standardized questionnaire. Daily temperature was collected from all the students, between January 1 and March 31, 2014. A 1 : 2 individual matched case-control study was conducted to identify related risk factors on this epidemic. We interviewed all the cases and controls on their diet, physical activities and measures used for warming. RESULTS: A total of 407 case-students were identified, with an attack rate (AR) as 26.5%. The AR was 37.3% in girls, compared to 12.9% in boys. The difference was statistically significant (χ² = 115.1, P < 0.01). Boarding students had a higher AR (31.8%) than the commuting students (16.2%). The difference was statistically significant (χ² = 43.2, P < 0.01). In girls, boarding students had higher AR (46.1%) than those commuting students (18.5%). The difference was statistically significant (χ² = 61.4, P < 0.01). No statistically significant difference was found between boarding or commuting students in boys. Outdoor temperature was coming down from 23 °C on February 6 to 6 °C on February 13, but gradually rose to 23 °C on February 28. There was a positive relationship (r = 0.65, P = 0.002) noticed between daily maximum temperature and the number of cases during February 13-28. Results from this case-control study showed that factors as lacking physical activities (OR = 2.8, 95% CI: 1.5-5.6), feeling cold in bed (OR = 3.0, 95% CI: 1.3-7.0) and having experienced similar symptoms (OR = 3.4, 95% CI: 1.1-11.0) could increase the risk of this disease. CONCLUSION: This outbreak was possibly caused by the abrupt fluctuation of temperature within a short period.

[Descriptive epidemiological study on mental disabilities in China].

OBJECTIVE: To describe the prevalence and characteristics of mental disabilities in China. METHODS: The data from the Second National Sample Survey on Disability were analyzed with descriptive epidemiological method and the overall prevalence rates of mental disabilities were statistically calculated. RESULTS: Among 2 526 145 respondents, 15 155 of them more than 15 years old were diagnosed as mental disabilities, with the prevalence rate as 6.01‰. The prevalence rate of disabilities caused only by mental disorders was 4.57‰ with 11 501 more than 15 years old. The prevalence rate of disability caused only by mental disorders was 4.67‰ with 11 501 adults. Of the disability cases that exclusively caused by mental disorders, 64.58% of them were attributable to schizophrenia, schizotypal or delusional disorders, 6.28% were mood disorder, and 6.27% were epilepsy disability, followed by neurotic, stress-related and somatoform disorders (5.95%), dementia (5.19%), and other disabilities (less than 11.74%). Disabilities that attributable to schizophrenia, schizotypal and delusional disorders caused most severe impairments of functions in daily and social activities, followed by disabilities attributable to dementia, non-dementia organic mental disorder and epilepsy disability. Dementia caused the most severe grade of disability, accounted for 44.89% of all the cases. The data also showed that the disabilities attributable to mood disorder and neurotic, stress-related and somatoform disorder showed more impairments among mental disabilities. CONCLUSION: Prevalence of mental disability in the second sample survey was higher than that of the first survey. Schizophrenia accounted for most of the mental disabilities but dementia caused the severest disability among all the cases with mental problems. As two of main causes of mental disabilities, neurosis and anxiety disorders should also be paid attention to.