Estimation of SM backgrounds to SUSY search in the 1-lepton + jets + MET channel

The ATLAS Collaboration has reported the first results of the search for SUSY particles in 1-lepton + 3 jets + MET final states. An essential ingredient for these results is a reliable background estimation in the signal region, in particular of the ttbar, W+jets and QCD backgrounds. The estimation of these three backgrounds is explained in this paper. The ttbar and W+jets backgrounds are obtained from a background dominated control region and extrapolated to the signal region, whereas for the estimation of the QCD background a matrix method is used.Comment: Contribution to conference proceedings (46th Rencontres de Moriond on Electroweak Interactions and Unified Theories, La Thuile, Italy, 13 - 20 Mar 2011

Search for strongly interacting supersymmetric particles decaying to final states with an isolated lepton with the ATLAS detector at the LHC

Two analyses searching for squarks and gluinos which decay into final states with multiple jets, an isolated electron or muon and a large missing transverse energy are presented. Both rely on data taken by the ATLAS detector in pp collisions at a center-of-mass energy of 8 TeV at the LHC during 2012. The first analysis uses a subset of 5.8 fb-1 of this dataset, the other analysis uses the full statistics of 20.3 fb-1. Both analysis share the same methods regarding the triggers and the background estimation techniques. The two dominant backgrounds are ttbar and W+jets production. The ttbar and the W+jets backgrounds are estimated in a semi-data-driven method. The minor QCD multi-jet background is estimated in an entirely data-driven method. The final background estimates in the analyses are derived in a profile-log-likelihood fit. None of the analyses sees an excess beyond Standard Model expectations. The analysis of the partial dataset derives limits in a MSUGRA/CMSSM model with parameters A_0=0, tan(beta) = 10 and mu > 0 and excludes squarks and gluinos with masses below 1.2 TeV for equal squark and gluino masses. The analysis of the full dataset derives limits in simplified models and in a MSUGRA/CMSSM model with parameters A_0=-2 m_0, tan(beta) = 30 and mu > 0. Gluinos (squarks) with masses below 1.2 TeV (750 GeV) can be excluded for vanishing LSP masses in simplified models. Gluino masses below 1.2 TeV can be excluded for every m_0 value in the MSUGRA/CMSSM model

Rim Pathway-Mediated Alterations in the Fungal Cell Wall Influence Immune Recognition and Inflammation

ACKNOWLEDGMENTS We acknowledge Jennifer Lodge, Woei Lam, and Rajendra Upadhya for developing and sharing the chitin and chitosan MTBH assay. We thank Todd Brennan of Duke University for providing MyD88-deficient mice. We acknowledge Neil Gow for providing access to the Dionex HPAEC-PAD instrumentation. We also acknowledge Connie Nichols for critical reading of the manuscript. These experiments were supported by an NIH grant to J.A.A. and F.L.W., Jr. (R01 AI074677). C.M.L.W. was supported by a fellowship provided through the Army Research Office of the Department of Defense (no. W911NF-11-1-0136 f) (F.L.W., Jr.). J.W., L.W., and C.M. were supported by the Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377) and the MRC, Centre for Medical Mycology (MR/N006364/1). FUNDING INFORMATION MRC Centre for Medical MycologyMR/N006364/1 Carol A. Munro HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) https://doi.org/10.13039/100000060R01 AI074677J. Andrew Alspaugh Wellcome https://doi.org/10.13039/100010269097377 Carol A. Munro DOD | United States Army | RDECOM | Army Research Office (ARO) https://doi.org/10.13039/100000183W911NF-11-1-0136 f Chrissy M. Leopold WagerPeer reviewe

Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset

In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This model is a symbiosis of a classical Convolutional NN that performs ultra-sound image processing and a quantum circuit that generates its stochastic weights, within a Bayesian learning framework. To test the utility of this idea for the possible future deployment in the medical sector we track multiple behavioral metrics that capture both predictive performance as well as model's uncertainty. It is our ambition to create a hybrid model that is capable to classify samples in a more uncertainty aware fashion, which will advance the trustworthiness of these models and thus bring us step closer to utilizing them in the industry. We test multiple setups for quantum circuit for this task, and our best architectures display bigger uncertainty gap between correctly and incorrectly identified samples than its classical benchmark at an expense of a slight drop in predictive performance. The innovation of this paper is two-fold: (1) combining of different approaches that allow the stochastic weights from the quantum circuit to be continues thus allowing the model to classify application-driven dataset; (2) studying architectural features of quantum circuit that make-or-break these models, which pave the way into further investigation of more informed architectural designs

Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning

With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident. In this work, we outline the connection between quantum noise channels and differential privacy (DP), by constructing a family of noise channels which are inherently $\epsilon$-DP: $(\alpha, \gamma)$-channels. Through this approach, we successfully replicate the $\epsilon$-DP bounds observed for depolarizing and random rotation channels, thereby affirming the broad generality of our framework. Additionally, we use a semi-definite program to construct an optimally robust channel. In a small-scale experimental evaluation, we demonstrate the benefits of using our optimal noise channel over depolarizing noise, particularly in enhancing adversarial accuracy. Moreover, we assess how the variables $\alpha$ and $\gamma$ affect the certifiable robustness and investigate how different encoding methods impact the classifier's robustness

Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis

As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomized smoothing, how data encoding and perturbation modeling approaches can be matched to achieve meaningful robustness certificates. By utilizing an innovative approach integrating Grover's algorithm, a quadratic sampling advantage over classical randomized smoothing is achieved. This strategy necessitates a basis state encoding, thus restricting the space of meaningful perturbations. We show how constrained $k$-distant Hamming weight perturbations are a suitable noise distribution here, and elucidate how they can be constructed on a quantum computer. The efficacy of the proposed framework is demonstrated on a time series classification task employing a Bag-of-Words pre-processing solution. The advantage of quadratic sample reduction is recovered especially in the regime with large number of samples. This may allow quantum computers to efficiently scale randomized smoothing to more complex tasks beyond the reach of classical methods.Comment: Accepted at the IEEE International Conference on Quantum Computing and Engineering (QCE

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a $\ell_2$-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of $\sim 13 \% / 5\%$ relative to previous approaches

Benchmarking the Variational Quantum Eigensolver using different quantum hardware

The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theoretical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the amount of iterations required. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation.Comment: Submitted to the IEEE for possible publicatio

Recommending Solution Paths for Solving Optimization Problems with Quantum Computing

Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed automation layer within a quantum-assisted solution workflow for optimization problems.Comment: Reviewed and published at IEEE QSW 202

Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization

Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph Neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous works. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA