Near-Optimal Quantum Algorithms for Multivariate Mean Estimation

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at extending the theory of multivariate sub-Gaussian estimators to the quantum setting. Unlike classically, where any univariate estimator can be turned into a multivariate estimator with at most a logarithmic overhead in the dimension, no similar result can be proved in the quantum setting. Indeed, Heinrich ruled out the existence of a quantum advantage for the mean estimation problem when the sample complexity is smaller than the dimension. Our main result is to show that, outside this low-precision regime, there is a quantum estimator that outperforms any classical estimator. Our approach is substantially more involved than in the univariate setting, where most quantum estimators rely only on phase estimation. We exploit a variety of additional algorithmic techniques such as amplitude amplification, the Bernstein-Vazirani algorithm, and quantum singular value transformation. Our analysis also uses concentration inequalities for multivariate truncated statistics. We develop our quantum estimators in two different input models that showed up in the literature before. The first one provides coherent access to the binary representation of the random variable and it encompasses the classical setting. In the second model, the random variable is directly encoded into the phases of quantum registers. This model arises naturally in many quantum algorithms but it is often incomparable to having classical samples. We adapt our techniques to these two settings and we show that the second model is strictly weaker for solving the mean estimation problem. Finally, we describe several applications of our algorithms, notably in measuring the expectation values of commuting observables and in the field of machine learning.Comment: 35 pages, 1 figure; v2: minor change

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. Research in this area has focused primarily on variational quantum algorithms (VQAs), and several proposals to enhance supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs have been put forward. Out of the three, RL is the least studied and it is still an open question whether VQAs can be competitive with state-of-the-art classical algorithms based on neural networks (NNs) even on simple benchmark tasks. In this work, we introduce a training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm. We investigate which architectural choices for quantum Q-learning agents are most important for successfully solving certain types of environments by performing ablation studies for a number of different data encoding and readout strategies. We provide insight into why the performance of a VQA-based Q-learning algorithm crucially depends on the observables of the quantum model and show how to choose suitable observables based on the learning task at hand. To compare our model against the classical DQN algorithm, we perform an extensive hyperparameter search of PQCs and NNs with varying numbers of parameters. We confirm that similar to results in classical literature, the architectural choices and hyperparameters contribute more to the agents' success in a RL setting than the number of parameters used in the model. Finally, we show when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments. This work paves the way towards new ideas on how a quantum advantage may be obtained for real-world problems in the future.Algorithms and the Foundations of Software technolog

Neuro-GPT: Developing A Foundation Model for EEG

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG

Histiocytose langerhansienne multiviscerale avec atteinte auriculaire bilaterale a propos d’une observation

L’histiocytose langerhansienne multi-viscérale est une prolifération clonale des cellules de Langerhans, touchant plusieurs organes. Cette entité se voit surtout chez l’enfant. Dans ce travail, nous rappelons les aspects cliniques avec la fréquence d’atteinte oto-rhino-laryngologique, ainsi que les moyens de diagnostic et le traitement de cette affection rare. Nous présentons le cas d’un enfant âgé de 2 ans qui a été hospitalisé pour une pneumopathie interstitielle, associée à une otorrhée bilatérale. L’examen a montré un comblement des 2 conduits auditifs externes et des lésions cutanées squameuses. La biopsie a conclu à une histiocytose langerhansienne. Malgré la chimiothérapie, l’enfant est décédé après 11 mois.Mots-clés : Histiocytose langerhansienne, atteinte auriculaire

Recurrence of Uterine Rupture in a Pseudo-Unicornuate Uterus at 17 Weeks of Amenorrhea: Case Report and Literature Review

Pregnancy in a rudimentary horn is a very rare condition. It is responsible for several complications. Prognosis is reserved because the natural evolution generally leads to a cataclysmic uterine rupture at the beginning of the second trimester. Classically, the treatment after foetal extraction consists of ablation of the rudimentary horn and associated fallopian tube. We report the obstetric outcome of a patient with history of rudimentary uterine horn rupture, the treatment of which was ablation of the rudimentary horn

Gamma oscillations in V1 are correlated with GABA(A) receptor density: A multi-modal MEG and Flumazenil-PET study.

High-frequency oscillations in the gamma-band reflect rhythmic synchronization of spike timing in active neural networks. The modulation of gamma oscillations is a widely established mechanism in a variety of neurobiological processes, yet its neurochemical basis is not fully understood. Modeling, in-vitro and in-vivo animal studies suggest that gamma oscillation properties depend on GABAergic inhibition. In humans, search for evidence linking total GABA concentration to gamma oscillations has led to promising -but also to partly diverging- observations. Here, we provide the first evidence of a direct relationship between the density of GABA(A) receptors and gamma oscillatory gamma responses in human primary visual cortex (V1). By combining Flumazenil-PET (to measure resting-levels of GABA(A) receptor density) and MEG (to measure visually-induced gamma oscillations), we found that GABA(A) receptor densities correlated positively with the frequency and negatively with amplitude of visually-induced gamma oscillations in V1. Our findings demonstrate that gamma-band response profiles of primary visual cortex across healthy individuals are shaped by GABA(A)-receptor-mediated inhibitory neurotransmission. These results bridge the gap with in-vitro and animal studies and may have future clinical implications given that altered GABAergic function, including dysregulation of GABA(A) receptors, has been related to psychiatric disorders including schizophrenia and depression

Differential electrophysiological response during rest, self-referential, and non-self-referential tasks in human posteromedial cortex

The electrophysiological basis for higher brain activity during rest and internally directed cognition within the human default mode network (DMN) remains largely unknown. Here we use intracranial recordings in the human posteromedial cortex (PMC), a core node within the DMN, during conditions of cued rest, autobiographical judgments, and arithmetic processing. We found a heterogeneous profile of PMC responses in functional, spatial, and temporal domains. Although the majority of PMC sites showed increased broad gamma band activity (30-180 Hz) during rest, some PMC sites, proximal to the retrosplenial cortex, responded selectively to autobiographical stimuli. However, no site responded to both conditions, even though they were located within the boundaries of the DMN identified with resting-state functional imaging and similarly deactivated during arithmetic processing. These findings, which provide electrophysiological evidence for heterogeneity within the core of the DMN, will have important implications for neuroimaging studies of the DMN

Etude rhéologique et thermique d'une boucle de réfrigération secondaire par coulis d'hydrates

14e édition du congrès de la Société française du génie des procédés, Lyon, FRA, 08-/10/2013 - 10/10/2013International audienceLes fluides réfrigérants classiques sont néfastes pour l'environnement en raison de leur potentiel de réchauffement global (GWP), c'est pourquoi leur utilisation doit être réduite. L'une des solutions est d'employer des fluides frigoporteurs diphasiques, comme les coulis d'hydrates, pour transporter le froid. Le travail réalisé a pour objectif d'étudier les propriétés rhéologiques et thermiques des coulis de CO2. Le dispositif expérimental est constitué d'une boucle pilote permettant la circulation des fluides. Les hydrates sont formés par refroidissement à des températures de l'ordre de 275 K et des pressions allant jusqu'à 3 MPa. Les coefficients d'échange thermique locaux et moyens du coulis ont également été évalués par l'utilisation d'un tube chauffant. La rhéologie a montré que le coulis présentait un comportement de type rhéofluidifiant pour des fractions d'hydrates en volume allant jusqu'à 22 %. L'étude thermique a quant à elle montré que le coulis présentait des coefficients d'échange locaux de l'ordre de 2900 W.m-2.K-1 pour une fraction en hydrates de 19 %, ce qui est supérieur à l'eau et légèrement plus élevé que le coulis de glace. Ainsi, ces résultats permettent de mettre en évidence les bonnes capacités du coulis d'hydrates à stocker, à véhiculer et à restituer l'énergie emmagasinée

High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest

Abstract. The use of remote sensors (thermometers and cameras) to analyse crop water status in field conditions is fraught with several difficulties. In particular, average canopy temperature measurements are affected by the mixture of soil and green regions, the mutual shading of leaves and the variability of absorbed radiation. The aim of the study was to analyse how the selection of different 'regions of interest' (ROI) in canopy images affect the variability of the resulting temperature averages. Using automated image segmentation techniques we computed the average temperature in four nested ROI of decreasing size, from the whole image down to the sunlit fraction of a leaf located in the upper part of the canopy. The study was conducted on maize (Zea mays L.) at the flowering stage, for its large leaves and well structured canopy. Our results suggest that, under these conditions, the ROI comprising the sunlit fraction of a leaf located in the upper part of the canopy should be analogous to the single leaf approach (in controlled conditions) that allows the estimation of stomatal conductance or plant water potential