A5091746505- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Functional Brain Connectivity Studies
- Quantum many-body systems
- Quantum and electron transport phenomena
- Allergic Rhinitis and Sensitization
- Machine Learning in Healthcare
- Lymphadenopathy Diagnosis and Analysis
- Vasculitis and related conditions
- Generative Adversarial Networks and Image Synthesis
- Child Nutrition and Feeding Issues
- Neural Networks and Reservoir Computing
- Sleep and Wakefulness Research
- AI in cancer detection
- Advanced Bandit Algorithms Research
- Schizophrenia research and treatment
- Machine Learning in Materials Science
- Advanced Multi-Objective Optimization Algorithms
- Sleep and related disorders
- Gaussian Processes and Bayesian Inference
- Domain Adaptation and Few-Shot Learning
Technical University of Denmark
2020-2023
University of Oxford
2023
Stanford University
2021
Preparing thermal states on a quantum computer can have variety of applications, from simulating many-body systems to training machine learning models. Variational circuits been proposed for this task near-term computers, but several challenges remain, such as finding scalable cost-function, avoiding the need purification, and mitigating noise effects. We propose new algorithm state preparation that tackles those three by exploiting circuits. consider variational architecture containing...
The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In multimodal neuropsychiatric dataset antipsychotic-naïve, first-episode schizophrenia patients, we discuss workflow aimed at reducing bias and overfitting by invoking simulated data the design process analysis two independent approaches, one based on single algorithm other incorporating an ensemble algorithms. We to (1) classify patients from controls establish framework, (2) predict short-...
Abstract Shallow quantum circuits are believed to be the most promising candidates for achieving early practical advantage—this has motivated development of a broad range error mitigation techniques whose performance generally improves when state is well approximated by global depolarising (white) noise model. While it been crucial demonstrating supremacy that random scramble local into white noise—a property proved rigorously—we investigate what degree shallow noise. We define two key...
The purpose of this study was to investigate seasonal variation in cases biopsy-proven GCA eastern Denmark a 29-year period.Pathology records all temporal artery biopsies between 1990 and 2018 were reviewed. For each patient, data collected which included age, sex, date birth biopsy result. Seasonality evaluated using logistic regression Poisson analysis. Lastly, an explorative pilot conducted possible association three weather parameters (average temperature, amount rain hours sunshine) the...
Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding experiment to perform next. However, not much work has addressed the effect performance BO algorithm and what extent calibrated uncertainties improve ability find global optimum. In this work, we provide an extensive study relationship between (regret) calibration surrogate models compare them across both synthetic real-world experiments....
The current study of human-machine alignment aims at understanding the geometry latent spaces and correspondence to human representations. G\"ardenfors' conceptual is a prominent framework for Convexity object regions in argued promote generalizability, few-shot learning, intersubject alignment. Based on these insights, we investigate notion convexity concept machine-learned spaces. We develop set tools measuring sampled data evaluate emergent layered representations state-of-the-art deep...
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 were also parameters extracted from polysomnographic recordings in three studies.The study sample (total n = 2,770) was composed the Wisconsin Sleep Cohort (n 1,091) Osteoporotic Fractures Men 1,026) two population-based studies, Stanford Cohort, a center patient-based 653). Seven features...
Shallow quantum circuits are believed to be the most promising candidates for achieving early practical advantage - this has motivated development of a broad range error mitigation techniques whose performance generally improves when state is well approximated by global depolarising (white) noise model. While it been crucial demonstrating supremacy that random scramble local into white property proved rigorously we investigate what degree shallow noise. We define two key metrics as (a)...
Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale (NISQ) devices. Testing these relevant hardware is crucial to investigate the effect noise and imperfections assess their practical value. Here, we implement variational algorithm designed optimized parameter estimation continuous variable platform based squeezed light, key component high-precision optical phase estimation. We ability identify...
Abstract Background The treatment response of patients with schizophrenia is heterogeneous, and markers clinical are missing. Studies using machine learning approaches have provided encouraging results regarding prediction outcomes, but replicability has been challenging. In the present study, we a novel methodological framework for applying to data. Herein, algorithm selection other choices were based on model performance simulated dataset, minimize bias avoid overfitting. We subsequently...
Preparing thermal states on a quantum computer can have variety of applications, from simulating many-body systems to training machine learning models. Variational circuits been proposed for this task near-term computers, but several challenges remain, such as finding scalable cost-function, avoiding the need purification, and mitigating noise effects. We propose new algorithm state preparation that tackles those three by exploiting circuits. consider variational architecture containing...