Background:Telemedicine (TM) contributes to bridge the gap between healthcare facilities and patients’ homes with neuromuscular disease (NMD) because of mobility issues. However, its deployment is... Show moreBackground:Telemedicine (TM) contributes to bridge the gap between healthcare facilities and patients’ homes with neuromuscular disease (NMD) because of mobility issues. However, its deployment is limited due to difficulties evaluating subtle neurological signs such as mild weakness or sensory deficits. The COVID-19 pandemic has disrupted healthcare delivery worldwide, necessitating rapid measures implementation by health care providers (HCPs) to protect patients from acquiring SARS-CoV-2 while maintaining the best care and treatment.Objectives:Given the challenges faced by remote healthcare assistance of NMD patients, we aim to evaluate the use of TM in NMD during the COVID-19 pandemic.Methods:Based on the Model for Assessment-of-Telemedicine-Applications (MAST), we conducted a survey amongst clinicians of the ERN EURO NMD (European-Reference-Network-for-Rare-Neuromuscular-Diseases).Results:Based on 42 responses over 76 expected ones, our results show that the COVID-19 pandemic significantly increased the number of HCPs using TM (from 60% to 100%). The TM types most used during the COVID-19 period are teleconsultation and consultation by phone, particularly in the context of symptoms worsening in NMD patients with COVID-19 infection. Most European HCPs were satisfied when using TM but as a complementary option to physical consultations. Many responses addressed the issue of technical aspects needing improvement, particularly for elderly patients who need caregivers’ assistance for accessing the TM platform.Conclusions:TM has been essential during COVID-19, but its use still presents some limitations for NMD patients with cognitive deficits or for first-time diagnosis. Thus, TM should be used as complement to, rather than substitute, for face-to-face consultations. Show less
Quantum algorithms for solving the Quantum Linear System (QLS) problem are among the most investigated quantum algorithms of recent times, with potential applications including the solution of... Show moreQuantum algorithms for solving the Quantum Linear System (QLS) problem are among the most investigated quantum algorithms of recent times, with potential applications including the solution of computationally intractable differential equations and speed-ups in machitie learning. A fundamental parameter governing the efficiency of QLS solvers is is, the condition number of the coefficient matrix A, as it has been known since the inception of the QLS problem that for worst-case instances the runtime scales at least linearly in kappa [1]. However, for the case of positive-definite matrices classical algorithms can solve linear systems with a runtime scaling as root kappa, a quadratic improvement compared to the the indefinite case. It is then natural to ask whether QLS solvers may hold an analogous improvement. In this work we answer the question in the negative, showing that solving a QLS entails a runtime linear in kappa also when A is positive definite. We then identify broad classes of positive-definite QLS where this lower bound can be circumvented and present two new quantum algorithms featuring a quadratic speed-up in kappa: the first is based on efficiently implementing a matrix-block-encoding of A(-1), the second constructs a decomposition of the form A=LL dagger to precondition the system. These methods are widely applicable and both allow to efficiently solve BQP-complete problems. Show less
