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Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i.e., to understand the generalization power of a model. Various capacity measures try to capture this ability, but usually fall short in explaining important characteristics of models that we observe in practice. In this study, we propose the local effective dimension as a capacity measure which seems to correlate well with generalization error on standard data sets. Importantly, we prove that the local effective dimension bounds the generalization error and discuss the aptness of this capacity measure for machine learning models.

Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of superposition to store sets of parameters, thereby creating an ensemble of quantum classifiers that may be computed in parallel. The idea stems from classical ensemble methods where one attempts to build a stronger model by averaging the results from many different models. In this work, we demonstrate that a specific implementation of the quantum ensemble of quantum classifiers, called the accuracy-weighted quantum ensemble, can be fully dequantised. On the other hand, the general quantum ensemble framework is shown to contain the well-known Deutsch-Jozsa algorithm that notably provides a quantum speedup and creates the potential for a useful quantum ensemble to harness this computational advantage.

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Background: Saudi Arabia implemented a nationwide lockdown to slow the spread of the COVID-19 after a global pandemic has been declared by the World Health Organization. Diabetes patients are one of the most vulnerable chronic illness groups to the complications of COVID-19 virus, thus the necessary to implement a tele-medicine clinic during the lockdown. Methods: A cross-sectional observational study, the study was done during the period from October to December 2020. We used convenience sampling to select participants who attended the clinics of the Diabetes Care Center at King Salman Hospital, Riyadh, Saudi Arabia. A total of 375 patients participated in the study. Results: The study included 375 participants around 60% were female participants. The age of almost one-third of them (33.9%) ranged between 51 and 60 years. Most of the participants were type 2 diabetic patients (85.3%), and lived in Riyadh city (97.6%). Vast majority of participants (99.5%) were follow-up patients and reported telemedicine visit by physicians (98.9%). Patients’ satisfaction questions showed that majority of the participants either strongly agreed or agreed with the statements that they were satisfied with the quality of the audio during the virtual visit (92%), use of telemedicine was essential in maintaining health during the COVID-19 outbreak (90.1%), the quality of the medical care provided during the virtual visit (88.3%), the clarity of the management plan discussed with the heath care practitioner during the virtual visit (87.7%), the tele-medicine visit was as good as a regular in person visit (81.5%). Also, majority of the participants recommend making diabetes tele-medicine clinic as an available option for patients with diabetes after the COVID-19 outbreak is over (81.5%). Conclusion: The COVID-19 pandemic has urged the transition from in person clinical visits to tele-medicine clinics and showed that it is feasible and effective to have the option of tele-medicine for diabetes clinics in Saudi Arabia. The majority of diabetic patients reported high levels of satisfaction with the tele-medicine clinic.