Shadows of quantum machine learning.

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a 'shadow model' from which the classical deployment becomes possible. We prove that: (i) this class of models is universal for classically-deployed quantum machine learning; (ii) it does have restricted learning capacities compared to 'fully quantum' models, but nonetheless (iii) it achieves a provable learning advantage over fully classical learners, contingent on widely believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.

Reinforcement learning assisted recursive QAOA.

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

Quantum machine learning beyond kernel methods.

Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models, remains limited. In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models. In particular, we show using tools from quantum information theory how data re-uploading circuits, an apparent outlier of this framework, can be efficiently mapped into the simpler picture of linear models in quantum Hilbert spaces. Furthermore, we analyze the experimentally-relevant resource requirements of these models in terms of qubit number and amount of data needed to learn. Based on recent results from classical machine learning, we prove that linear quantum models must utilize exponentially more qubits than data re-uploading models in order to solve certain learning tasks, while kernel methods additionally require exponentially more data points. Our results provide a more comprehensive view of quantum machine learning models as well as insights on the compatibility of different models with NISQ constraints.

Neonatal cardiogenic shock revealing obstructive cardiac Hibernoma: case report.

BACKGROUND: Cardiac Hibernomas are very rare benign tumors and usually remain asymptomatic. Neonatal cardiogenic shock due to cardiac tumors is extremely very rare. Until this date a few cases of cardiac hibernoma have been reported in the literature. Transthoracic echocardiography help in the differential diagnosis, but the definitive diagnosis is histological. The management strategy is not clearly codified. The Aim is to report and discuss the clinical features of a cardiac Hibernoma and review the relevant literature. CASE PRESENTATION: We describe a case of a 2-day-old Caucasian full-term male neonate admitted in neonate intensive care with cardiogenic shock, having fluid resuscitation and inotropic drugs. Ventilatory support was started immediately with the subsequent reestablishment of normal blood pressure. Then he was transferred to the echocardiography laboratory. Transthoracic echocardiography showed two echogenic masses in the right atrium and right ventricle. The masses were extended to the pulmonary trunk. Pulmonary artery flow measurements showed the presence of pulmonary and tricuspid obstruction. Surgery was rapidly considered since the baby was hemodynamically unstable. Intraoperative evaluation showed a mass embedded in the interventricular septum that occupy the right ventricular cavity and the right atrium. The tumor involved also the chordae of the tricuspid. Partial resection was done. Tricuspid valve repair was performed by construction of new chordae from the autologous pericardium. The specimen was sent for histopathological analysis. The baby died immediately after surgery. Histological examination of the surgical specimen revealed clear multivacuolated cells filled with lipid droplets and granular intense eosinophilic cytoplasm which confirms the diagnosis of Hibernoma. CONCLUSION: Cardiac Hibernomas are rare benign tumors. The prognosis and treatment strategy is closely dependent on the location, initial clinical presentation and possible complications. The prognosis can be unfavorable if the tumor was obstructive and infiltrate the myocardium.

A large cardiac hydatid cyst in the interventricular septum: A case report.

Isolated cardiac location is an uncommon presentation of echinococcosis (0.5-2%), and involvement of the interventricular septum is even rarer. It may lead to various complications because of rupture and embolization. We report the case of a 26 - year- old man who was diagnosed to have a large inter-ventricular hydatid cyst complicated by both cerebral and coronary embolism. Presentation, management and follow-up of the patient is discussed. This case is of particular interest because of the rarity of septal localization of a hydatid cyst, and the conflict between the severity of the complications that occurred and the absence of correlated symptoms.

Genomic analysis of in vivo acquired resistance to colistin and rifampicin in Acinetobacter baumannii.

Acinetobacter baumannii is an opportunistic pathogen in healthcare facilities responsible for nosocomial infections mostly in immunocompromised patients. Colistin resistance is increasingly reported worldwide in A. baumannii. Here we describe the in vivo selection of colistin and rifampicin resistance in carbapenem-resistant A. baumannii. Antimicrobial susceptibility testing, plasmid analysis and whole-genome sequencing (WGS) were performed to fully characterise the resistome of two clinical isolates (AbS1 and AbS2) selected during treatment. Clinical isolate AbS1 remained susceptible to colistin, rifampicin and tigecycline, whilst AbS2 was susceptible only to tigecycline. PCR analysis revealed the presence of a blaOXA-23-like carbapenemase gene. Kieser extraction revealed an ca. 74 kb plasmid harbouring blaOXA-23. WGS revealed genomes of 3.8 Mbp in size with a G + C content of 38.9%, and both belonged to ST281 according to the Oxford MLST scheme and ST641 according to the Institut Pasteur scheme. The resistome was also composed of naturally occurring ß-lactamases, i.e. ADC-25 cephalosporinase and OXA-82 oxacillinase, aminoglycoside resistance genes [aac(3)-Ia, aadA1 and aph(3')-VIa (aphA6)], and mutations in DNA gyrases explaining fluoroquinolone resistance. Single nucleotide polymorphism analysis revealed that both isolates were identical except for a 30-nucleotide duplication within the pmrB gene and a point mutation in the rpoB gene resulting in colistin and rifampicin resistance, respectively. This study highlights the genomic plasticity of A. baumannii under antibiotic pressure. The 10-amino acid duplication in PmrB affects colistin susceptibility by regulating lipopolysaccharide modification through the PmrAB two-component system. These findings provide further information on the molecular mechanisms leading to colistin resistance in A. baumannii.

[Cor triatriatum: of six operated patients report]. / Coeur triatrial: a propos de 6 cas opérés.

Six cases of cortriatriatum operated between January 1990 and April 2004. Their ages ranged from 3 months to 22 years. Associated lesions are present in 4 patients. They consisted mostly of interatrial defects. The echocardiography has been considered the procedure of choice for the diagnosis. Results of surgery were excellent for all patients with a mean follow up of 5 years.