Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has supported the development of a multitude of software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate, and execute quantum algorithms. Most of them allow us to define quantum circuits, run basic quantum algorithms, and access low-level primitives depending on the hardware such software is supposed to run. For most experiments, these frameworks have to be manually integrated within a larger machine learning software pipeline. The researcher is in charge of knowing different software packages, integrating them through the development of long code scripts, analyzing the results, and generating the plots. Long code often leads to erroneous applications, due to the average number of bugs growing proportional with respect to the program length. Moreover, other researchers will struggle to understand and reproduce the experiment, due to the need to be familiar with all the different software frameworks involved in the code script. We propose QuASK, an open-source quantum machine learning framework written in Python that aids the researcher in performing their experiments, with particular attention to quantum kernel techniques. QuASK can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results. QuASK implements most state-of-the-art algorithms to analyze the data through quantum kernels, with the possibility to use projected kernels, (gradient-descent) trainable quantum kernels, and structure-optimized quantum kernels. Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.Comment: Close to the published versio

QuASK - Quantum Advantage Seeker with Kernels

QuASK is a quantum machine learning software written in Python that supports researchers in designing, experimenting, and assessing different quantum and classical kernels performance. This software is package agnostic and can be integrated with all major quantum software packages (e.g. IBM Qiskit, Xanadu's Pennylane, Amazon Braket). QuASK guides the user through a simple preprocessing of input data, definition and calculation of quantum and classical kernels, either custom or pre-defined ones. From this evaluation the package provides an assessment about potential quantum advantage and prediction bounds on generalization error. Moreover, it allows for the generation of parametric quantum kernels that can be trained using gradient-descent-based optimization, grid search, or genetic algorithms. Projected quantum kernels, an effective solution to mitigate the curse of dimensionality induced by the exponential scaling dimension of large Hilbert spaces, are also calculated. QuASK can furthermore generate the observable values of a quantum model and use them to study the prediction capabilities of the quantum and classical kernels

"Delirium Day": A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool

Background: To date, delirium prevalence in adult acute hospital populations has been estimated generally from pooled findings of single-center studies and/or among specific patient populations. Furthermore, the number of participants in these studies has not exceeded a few hundred. To overcome these limitations, we have determined, in a multicenter study, the prevalence of delirium over a single day among a large population of patients admitted to acute and rehabilitation hospital wards in Italy. Methods: This is a point prevalence study (called "Delirium Day") including 1867 older patients (aged 65 years or more) across 108 acute and 12 rehabilitation wards in Italian hospitals. Delirium was assessed on the same day in all patients using the 4AT, a validated and briefly administered tool which does not require training. We also collected data regarding motoric subtypes of delirium, functional and nutritional status, dementia, comorbidity, medications, feeding tubes, peripheral venous and urinary catheters, and physical restraints. Results: The mean sample age was 82.0 ± 7.5 years (58 % female). Overall, 429 patients (22.9 %) had delirium. Hypoactive was the commonest subtype (132/344 patients, 38.5 %), followed by mixed, hyperactive, and nonmotoric delirium. The prevalence was highest in Neurology (28.5 %) and Geriatrics (24.7 %), lowest in Rehabilitation (14.0 %), and intermediate in Orthopedic (20.6 %) and Internal Medicine wards (21.4 %). In a multivariable logistic regression, age (odds ratio [OR] 1.03, 95 % confidence interval [CI] 1.01-1.05), Activities of Daily Living dependence (OR 1.19, 95 % CI 1.12-1.27), dementia (OR 3.25, 95 % CI 2.41-4.38), malnutrition (OR 2.01, 95 % CI 1.29-3.14), and use of antipsychotics (OR 2.03, 95 % CI 1.45-2.82), feeding tubes (OR 2.51, 95 % CI 1.11-5.66), peripheral venous catheters (OR 1.41, 95 % CI 1.06-1.87), urinary catheters (OR 1.73, 95 % CI 1.30-2.29), and physical restraints (OR 1.84, 95 % CI 1.40-2.40) were associated with delirium. Admission to Neurology wards was also associated with delirium (OR 2.00, 95 % CI 1.29-3.14), while admission to other settings was not. Conclusions: Delirium occurred in more than one out of five patients in acute and rehabilitation hospital wards. Prevalence was highest in Neurology and lowest in Rehabilitation divisions. The "Delirium Day" project might become a useful method to assess delirium across hospital settings and a benchmarking platform for future surveys

Understanding Factors Associated With Psychomotor Subtypes of Delirium in Older Inpatients With Dementia

Objectives: Few studies have analyzed factors associated with delirium subtypes. In this study, we investigate factors associated with subtypes of delirium only in patients with dementia to provide insights on the possible prevention and treatments. Design: This is a cross-sectional study nested in the \u201cDelirium Day\u201d study, a nationwide Italian point-prevalence study. Setting and Participants: Older patients admitted to 205 acute and 92 rehabilitation hospital wards. Measures: Delirium was evaluated with the 4-AT and the motor subtypes with the Delirium Motor Subtype Scale. Dementia was defined by the presence of a documented diagnosis in the medical records and/or prescription of acetylcholinesterase inhibitors or memantine prior to admission. Results: Of the 1057 patients with dementia, 35% had delirium, with 25.6% hyperactive, 33.1% hypoactive, 34.5% mixed, and 6.7% nonmotor subtype. There were higher odds of having venous catheters in the hypoactive (OR 1.82, 95% CI 1.18-2.81) and mixed type of delirium (OR 2.23, CI 1.43-3.46), whereas higher odds of urinary catheters in the hypoactive (OR 2.91, CI 1.92-4.39), hyperactive (OR 1.99, CI 1.23-3.21), and mixed types of delirium (OR 2.05, CI 1.36-3.07). We found higher odds of antipsychotics both in the hyperactive (OR 2.87, CI 1.81-4.54) and mixed subtype (OR 1.84, CI 1.24-2.75), whereas higher odds of antibiotics was present only in the mixed subtype (OR 1.91, CI 1.26-2.87). Conclusions and Implications: In patients with dementia, the mixed delirium subtype is the most prevalent followed by the hypoactive, hyperactive, and nonmotor subtype. Motor subtypes of delirium may be triggered by clinical factors, including the use of venous and urinary catheters, and the use of antipsychotics. Future studies are necessary to provide further insights on the possible pathophysiology of delirium in patients with dementia and to address the optimization of the management of potential risk factors

Drug prescription and delirium in older inpatients: Results from the nationwide multicenter Italian Delirium Day 2015-2016

Objective: This study aimed to evaluate the association between polypharmacy and delirium, the association of specific drug categories with delirium, and the differences in drug-delirium association between medical and surgical units and according to dementia diagnosis. Methods: Data were collected during 2 waves of Delirium Day, a multicenter delirium prevalence study including patients (aged 65 years or older) admitted to acute and long-term care wards in Italy (2015-2016); in this study, only patients enrolled in acute hospital wards were selected (n = 4,133). Delirium was assessed according to score on the 4 "A's" Test. Prescriptions were classified by main drug categories; polypharmacy was defined as a prescription of drugs from 5 or more classes. Results: Of 4,133 participants, 969 (23.4%) had delirium. The general prevalence of polypharmacy was higher in patients with delirium (67.6% vs 63.0%, P =.009) but varied according to clinical settings. After adjustment for confounders, polypharmacy was associated with delirium only in patients admitted to surgical units (OR = 2.9; 95% CI, 1.4-6.1). Insulin, antibiotics, antiepileptics, antipsychotics, and atypical antidepressants were associated with delirium, whereas statins and angiotensin receptor blockers exhibited an inverse association. A stronger association was seen between typical and atypical antipsychotics and delirium in subjects free from dementia compared to individuals with dementia (typical: OR = 4.31; 95% CI, 2.94-6.31 without dementia vs OR = 1.64; 95% CI, 1.19-2.26 with dementia; atypical: OR = 5.32; 95% CI, 3.44-8.22 without dementia vs OR = 1.74; 95% CI, 1.26-2.40 with dementia). The absence of antipsychotics among the prescribed drugs was inversely associated with delirium in the whole sample and in both of the hospital settings, but only in patients without dementia. Conclusions: Polypharmacy is significantly associated with delirium only in surgical units, raising the issue of the relevance of medication review in different clinical settings. Specific drug classes are associated with delirium depending on the clinical setting and dementia diagnosis, suggesting the need to further explore this relationship

Drug Prescription and Delirium in Older Inpatients: Results From the Nationwide Multicenter Italian Delirium Day 2015-2016

Objective: This study aimed to evaluate the association between polypharmacy and delirium, the association of specific drug categories with delirium, and the differences in drug-delirium association between medical and surgical units and according to dementia diagnosis. Methods: Data were collected during 2 waves of Delirium Day, a multicenter delirium prevalence study including patients (aged 65 years or older) admitted to acute and long-term care wards in Italy (2015-2016); in this study, only patients enrolled in acute hospital wards were selected (n = 4,133). Delirium was assessed according to score on the 4 "A's" Test. Prescriptions were classified by main drug categories; polypharmacy was defined as a prescription of drugs from 5 or more classes. Results: Of 4,133 participants, 969 (23.4%) had delirium. The general prevalence of polypharmacy was higher in patients with delirium (67.6% vs 63.0%, P =.009) but varied according to clinical settings. After adjustment for confounders, polypharmacy was associated with delirium only in patients admitted to surgical units (OR = 2.9; 95% CI, 1.4-6.1). Insulin, antibiotics, antiepileptics, antipsychotics, and atypical antidepressants were associated with delirium, whereas statins and angiotensin receptor blockers exhibited an inverse association. A stronger association was seen between typical and atypical antipsychotics and delirium in subjects free from dementia compared to individuals with dementia (typical: OR = 4.31; 95% CI, 2.94-6.31 without dementia vs OR = 1.64; 95% CI, 1.19-2.26 with dementia; atypical: OR = 5.32; 95% CI, 3.44-8.22 without dementia vs OR = 1.74; 95% CI, 1.26-2.40 with dementia). The absence of antipsychotics among the prescribed drugs was inversely associated with delirium in the whole sample and in both of the hospital settings, but only in patients without dementia. Conclusions: Polypharmacy is significantly associated with delirium only in surgical units, raising the issue of the relevance of medication review in different clinical settings. Specific drug classes are associated with delirium depending on the clinical setting and dementia diagnosis, suggesting the need to further explore this relationship