An Application of Reinforcement Learning for Minor Embedding in Quantum Annealing

Research in the Quantum Computing (QC) field has been soaring thanks to the latest developments and wider availability of real hardware. The strong interest in this technology has naturally spurred a contamination with the Machine Learning (ML) field. Both quantum methods to perform ML and ML methods to support quantum computation has been developed. A largely diffused QC paradigm is that of Quantum Annealers, machines that can rapidly search for solutions to optimization problems. Their sparse qubit structure, however, requires to search for a mapping between the problem’s and the hardware’s graphs before computation. This is a NP-hard combinatorial optimization task in itself, called Minor Embedding. In this work, we aim at developing and assessing the capabilities of Reinforcement Learning to perform this task

Towards Recommender Systems with Community Detection and Quantum Computing

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves

Reinforcement Learning for Variational Quantum Circuit Design

Variational Quantum Algorithms (VQAs) are widely used for solving optimization problems in the Noisy Intermediate-Scale Quantum (NISQ) era. However, designing effective quantum circuits (ansatzes) that are compatible with the limitations of current quantum hardware remains a significant challenge. In this work, we introduce a Reinforcement Learning (RL) agent that autonomously generates ansatzes for VQAs. The RL agent is trained on several optimization problems, including Maximum Cut, Maximum Clique, and Minimum Vertex Cover, across different graph topologies. Our results show that the agent is able to generate effective quantum circuits, with approximation ratios that favorably compare to commonly used ansatzes. Additionally, we identify a novel family of ansatzes, termed “Ryz-connected”, particularly effective on Maximum Cut problems. These findings highlight the potential of RL techniques in designing efficient quantum circuits for a broad class of applications in quantum computing

Synthetic Nanoparticles for Vaccines and Immunotherapy

The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugs– recombinant proteins (especially antibodies) or patient-derived cell therapies– but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004

Une ambiance lumineuse en fonction des besoins

Presentation of the ECAL-EPFL collaborative project in user-robot swarm interaction

CD8 positive T cells express IL-17 in patients with chronic obstructive pulmonary disease

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory disease of the lung. The nature of the immune reaction in COPD raises the possibility that IL-17 and related cytokines may contribute to this disorder. This study analyzed the expression of IL-17A and IL-17F as well as the phenotype of cells producing them in bronchial biopsies from COPD patients.</p> <p>Methods</p> <p>Bronchoscopic biopsies of the airway were obtained from 16 COPD subjects (GOLD stage 1-4) and 15 control subjects. Paraffin sections were used for the investigation of IL-17A and IL-17F expression in the airways by immunohistochemistry, and frozen sections were used for the immunofluorescence double staining of IL-17A or IL-17F paired with CD4 or CD8. In order to confirm the expression of IL-17A and IL-17F at the mRNA level, a quantitative RT-PCR was performed on the total mRNA extracted from entire section or CD8 positive cells selected by laser capture microdissection.</p> <p>Results</p> <p>IL-17F immunoreactivity was significantly higher in the bronchial biopsies of COPD patients compared to control subjects (<it>P </it>< 0.0001). In the submucosa, the absolute number of both IL-17A and IL-17F positive cells was higher in COPD patients (<it>P </it>< 0.0001). After adjusting for the total number of cells in the submucosa, we still found that more cells were positive for both IL-17A (<it>P </it>< 0.0001) and IL-17F (<it>P </it>< 0.0001) in COPD patients compared to controls. The mRNA expression of IL-17A and IL-17F in airways of COPD patients was confirmed by RT-PCR. The expression of IL-17A and IL-17F was co-localized with not only CD4 but also CD8, which was further confirmed by RT-PCR on laser capture microdissection selected CD8 positive cells.</p> <p>Conclusion</p> <p>These findings support the notion that Th17 cytokines could play important roles in the pathogenesis of COPD, raising the possibility of using this mechanism as the basis for novel therapeutic approaches.</p

Mottness at finite doping and charge instabilities in cuprates

The intrinsic instability of underdoped copper oxides towards inhomogeneous states is one of the central puzzles of the physics of correlated materials. The influence of the Mott physics on the doping-temperature phase diagram of copper oxides represents a major issue that is subject of intense theoretical and experimental effort. Here, we investigate the ultrafast electron dynamics in prototypical single-layer Bi-based cuprates at the energy scale of the O-2p\u2192Cu-3d charge-transfer (CT) process. We demonstrate a clear evolution of the CT excitations from incoherent and localized, as in a Mott insulator, to coherent and delocalized, as in a conventional metal. This reorganization of the high-energy degrees of freedom occurs at the critical doping pcr 430.16 irrespective of the temperature, and it can be well described by dynamical mean field theory calculations. We argue that the onset of the low-temperature charge instabilities is the low-energy manifestation of the underlying Mottness that characterizes the p<pcr region of the phase diagram. This discovery sets a new framework for theories of charge order and low-temperature phases in underdoped copper oxides. ArXI

cIAP1/2 Are Direct E3 Ligases Conjugating Diverse Types of Ubiquitin Chains to Receptor Interacting Proteins Kinases 1 to 4 (RIP1–4)

The RIP kinases have emerged as essential mediators of cellular stress that integrate both extracellular stimuli emanating from various cell-surface receptors and signals coming from intracellular pattern recognition receptors. The molecular mechanisms regulating the ability of the RIP proteins to transduce the stress signals remain poorly understood, but seem to rely only partially on their kinase activities. Recent studies on RIP1 and RIP2 have highlighted the importance of ubiquitination as a key process regulating their capacity to activate downstream signaling pathways. In this study, we found that XIAP, cIAP1 and cIAP2 not only directly bind to RIP1 and RIP2 but also to RIP3 and RIP4. We show that cIAP1 and cIAP2 are direct E3 ubiquitin ligases for all four RIP proteins and that cIAP1 is capable of conjugating the RIPs with diverse types of ubiquitin chains, including linear chains. Consistently, we show that repressing cIAP1/2 levels affects the activation of NF-κB that is dependent on RIP1, -2, -3 and -4. Finally, we identified Lys51 and Lys145 of RIP4 as two critical residues for cIAP1-mediated ubiquitination and NF-κB activation

LRRK2 protein levels are determined by kinase function and are crucial for kidney and lung homeostasis in mice

Mutations in leucine-rich repeat kinase 2 (LRRK2) cause late-onset Parkinson's disease (PD), but the underlying pathophysiological mechanisms and the normal function of this large multidomain protein remain speculative. To address the role of this protein in vivo, we generated three different LRRK2 mutant mouse lines. Mice completely lacking the LRRK2 protein (knock-out, KO) showed an early-onset (age 6 weeks) marked increase in number and size of secondary lysosomes in kidney proximal tubule cells and lamellar bodies in lung type II cells. Mice expressing a LRRK2 kinase-dead (KD) mutant from the endogenous locus displayed similar early-onset pathophysiological changes in kidney but not lung. KD mutants had dramatically reduced full-length LRRK2 protein levels in the kidney and this genetic effect was mimicked pharmacologically in wild-type mice treated with a LRRK2-selective kinase inhibitor. Knock-in (KI) mice expressing the G2019S PD-associated mutation that increases LRRK2 kinase activity showed none of the LRRK2 protein level and histopathological changes observed in KD and KO mice. The autophagy marker LC3 remained unchanged but kidney mTOR and TCS2 protein levels decreased in KD and increased in KO and KI mice. Unexpectedly, KO and KI mice suffered from diastolic hypertension opposed to normal blood pressure in KD mice. Our findings demonstrate a role for LRRK2 in kidney and lung physiology and further show that LRRK2 kinase function affects LRRK2 protein steady-state levels thereby altering putative scaffold/GTPase activity. These novel aspects of peripheral LRRK2 biology critically impact ongoing attempts to develop LRRK2 selective kinase inhibitors as therapeutics for PD