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Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realization for practical applications remains impeded by challenges. Among these, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any prospective speedups over classical algorithms. In this study, we implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic, and matrix product state algorithms. Our results show that these methods can approximately prepare states to a level suitable for QML using circuits 2 orders of magnitude shallower than a standard state preparation implementation, thus drastically reducing circuit depth and gate count without unduly sacrificing classification accuracy. Additionally, the QML models trained and evaluated on approximately encoded data displayed an increased robustness to adversarially generated input data perturbations. This partial alleviation of adversarial vulnerability, made possible by the retention of the meaningful large-scale features of the data despite the “drowning out” of adversarial perturbations, constitutes a considerable benefit for approximate state preparation in addition to lessening the requirements of the quantum hardware. Our results, based on simulations and experiments on IBM quantum devices, highlight a promising pathway for the future implementation of accurate and robust QML models on complex datasets relevant for practical applications, bringing the possibility of NISQ-era QML advantage closer to reality.

Fernanda Pilaquinga,1,2 Jeroni Morey,2 Lenys Fernandez,1 Patricio Espinoza-Montero,1 Mauricio Moncada-Basualto,3 Josue Pozo-Martinez,3 Claudio Olea-Azar,3 Rafael Bosch,4 Lorena Meneses,1 Alexis Debut,5 María de las Nieves Piña2 1School of Chemical Sciences, Pontificia Universidad Católica del Ecuador, Quito, Ecuador; 2Department of Chemistry, University of the Balearic Islands, Palma de Mallorca, Spain; 3Department of Inorganic and Analytical Chemistry, University of Chile, Santiago, Chile; 4Environmental Microbiology, IMEDEA (CSIC-UIB), and Microbiology, Department of Biology, University of Balearic Islands, Palma de Mallorca, Spain; 5Centro de Nanociencia y Nanotecnología, Universidad de las Fuerzas Armadas ESPE, Sangolquí, EcuadorCorrespondence: Fernanda PilaquingaSchool of Chemical Sciences, Pontificia Universidad Católica del Ecuador, Quito, EcuadorTel +593 2 7427510Email mfpilaquingaf@puce.edu.ecPurpose: The importance of studying polyphenolic compounds as natural antioxidants has encouraged the search for new methods of analysis that are quick and simple. The synthesis of silver nanoparticles (AgNPs) using plant extracts has been presented as an alternative to determine the total polyphenolic content and its antioxidant activity.Methods: In this study, aqueous leaf extract of Solanum mammosum, a species of plant endemic to South America, was used to produce AgNPs. The technique of oxygen radical absorption capacity using fluorescein (ORAC-FL) was used to measure antioxidant activity. The oxidation of the 2´,7´-dichlorodihydrofluorescein diacetate (DCFH2-DA) as fluorescent probe was used to measure cellular antioxidant activity (CAA). Electrochemical behavior was also examined using differential pulse voltammetry (DPV) and cyclic voltammetry (CV). Total polyphenolic content (TPH) was analyzed using the Folin-Ciocalteu method, and the major polyphenolic compound was analyzed by high performance liquid chromatography with diode array detector (HPLC/DAD). Finally, a microbial analysis was conducted using Escherichia coli and Bacillus sp.Results: The average size of nanoparticles was 5.2 ± 2.3 nm measured by high-resolution transmission electron microscopy (HR-TEM). The antioxidant activity measured by ORAC-FL in the extract and nanoparticles were 3944 ± 112 and 637.5 ± 14.8 μM ET/g of sample, respectively. Cellular antioxidant activity was 14.7 ± 0.2 for the aqueous extract and 12.5 ± 0.2 for the nanoparticles. The electrochemical index (EI) was 402 μA/V for the extract and 324 μA/V for the nanoparticles. Total polyphenolic content was 826.6 ± 20.9 and 139.7 ± 20.9 mg EGA/100 g of sample. Gallic acid was the main polyphenolic compound present in the leaf extract. Microbiological analysis revealed that although leaf extract was not toxic for Escherichia coli and Bacillus sp., minor toxic activity for AgNPs was detected for both strains.Conclusion: It is concluded that the aqueous extract of the leaves of S. mammosum contains nontoxic antioxidant compounds capable of producing AgNPs. The methods using AgNPs can be used as a fast analytical tool to monitor the presence of water-soluble polyphenolic compounds from plant origin. Analysis and detection of new antioxidants from plant extracts may be potentially applicable in biomedicine.Keywords: silver nanoparticles, Solanum mammosum, antioxidant activity, ORAC, voltammetry

Objective: The benefit of a nasal corticosteroid in the treatment of persistent post-infectious smell disorders is not as clear in previous studies as is assumed for olfactory training. This study would therefore like to describe the treatment strategies using the example of a persistent olfactory dysfunction as a result of a proven infection with SARS-CoViD-2-virus. Methods: Twenty patients (average age of 33.9 ± 11.9 years) with hyposmia were included in this study from December 2020 to July 2021. Every second patient received additionally a nasal corticosteroid. The two resulting randomized groups of equal size were screened with the TDI test, a 20-item taste powder test for the assessment of retronasal olfaction and otorhinolaryngological examination. The patients were asked to train twice daily using a standardized odor training kit and followed up after 2 months and 3 months, respectively. Results: We documented a significant overall improvement in olfactory ability over the investigation period in both groups. While the TDI score steadily increased on average under the combination therapy, the rise under olfactory training alone was initially steeper. This short-term interaction effect over mean two months was not statistically significant. According to Cohen, however, a moderate effect (eta 2 = 0.055, Cohen's d = 0.5) can still be assumed. This effect could be explained by a possibly higher compliance at the beginning of the sole olfactory training due to the lack of further drug treatment offers. When the training intensity decreases, the recovery of the sense of smell stagnates. Adjunctive therapy ultimately outweighs this short-term benefit. Conclusions: The results reinforce the recommendation of early and consistent olfactory training on patients with dysosmia due to COVID-19. For continuous improvement of the sense of smell, an accompanying topical treatment seems at least to be worth consideration. The results should be optimized with larger cohorts and using new objective olfactometric methods. [ABSTRACT FROM AUTHOR]