Noise-assisted variational quantum thermalization.

Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circuits have been proposed for this task on near-term quantum computers, but several challenges remain, such as finding a scalable cost-function, avoiding the need of purification, and mitigating noise effects. We propose a new algorithm for thermal state preparation that tackles those three challenges by exploiting the noise of quantum circuits. We consider a variational architecture containing a depolarizing channel after each unitary layer, with the ability to directly control the level of noise. We derive a closed-form approximation for the free-energy of such circuit and use it as a cost function for our variational algorithm. By evaluating our method on a variety of Hamiltonians and system sizes, we find several systems for which the thermal state can be approximated with a high fidelity. However, we also show that the ability for our algorithm to learn the thermal state strongly depends on the temperature: while a high fidelity can be obtained for high and low temperatures, we identify a specific range for which the problem becomes more challenging. We hope that this first study on noise-assisted thermal state preparation will inspire future research on exploiting noise in variational algorithms.

Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults.

STUDY OBJECTIVES: Subjective insomnia complaints and objective sleep changes are mostly studied outside of clinical trial studies. In this study, we tested whether 240 genetic variants associated with subjectively reported insomnia were also associated with objective insomnia parameters extracted from polysomnographic recordings in three studies. METHODS: The study sample (total n = 2,770) was composed of the Wisconsin Sleep Cohort (n = 1,091) and the Osteoporotic Fractures in Men (n = 1,026) study, two population-based studies, and the Stanford Sleep Cohort, a sleep center patient-based sample (n = 653). Seven objective polysomnographic features related to insomnia defined outcome variables, with each variant allele serving as predictor. Meta-regression was performed, accounting for common confounders as well as variance differences between studies. Additionally, a normalized genetic risk score was generated for each subject to serve as a predictor variable in separate linear mixed models assessing objective insomnia features. RESULTS: After correction for multiple testing, single-nucleotide polymorphisms associated with subjective insomnia were not significantly associated with 6 of 7 objective sleep measures. Only periodic limb movement index was significantly associated with rs113851554 (MEIS1), as found in previous studies. The normalized genetic risk score was only weakly associated with arousal index and duration of wake after sleep onset. CONCLUSIONS: Our findings suggest that subjective insomnia does not have a strong genetic signature mapping onto objective (polysomnographic) sleep variables. CITATION: Foldager J, Peppard PE, Hagen EW, et al. Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults. J Clin Sleep Med. 2022;18(1):21-29.

Seasonal variation in biopsy-proven giant cell arteritis in Eastern Denmark from 1990-2018.

PURPOSE: The purpose of this study was to investigate seasonal variation in cases of biopsy-proven GCA in eastern Denmark in a 29-year period. METHODS: Pathology records of all temporal artery biopsies in eastern Denmark between 1990 and 2018 were reviewed. For each patient, data were collected which included age, sex, date of birth and biopsy result. Seasonality was evaluated using logistic regression and Poisson regression analysis. Lastly, an explorative pilot study was conducted to investigate a possible association between three weather parameters (average temperature, amount of rain and hours of sunshine) and the biopsy outcome. RESULTS: One thousand three hundred twenty-three biopsies were included of which 336 fulfilled objective criteria for GCA diagnosis. Mean age at diagnosis was 75.6 years (range 52-94 years). Among the biopsy-proven cases of GCA, there were 223 women (66.3%, mean age 76.2 years) and 113 men (33.7%, mean age 74.4 years) giving a female to male ratio of 1.97:1. The peak occurrence of GCA was in the 70-79 years age group. Statistical analysis of seasonal variation showed an increased risk of a positive biopsy during summer compared to autumn (p = 0.037). No association between the three weather parameters and the biopsy outcome was found. CONCLUSION: In this study of biopsy-proven GCA in a large Danish patient cohort, the occurrence of GCA showed seasonal variation with higher occurrence in the summer months when compared to autumn. Future studies pooling all cases of GCA worldwide are needed to determine seasonality in the occurrence of GCA.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.