Digital-Analog Quantum Simulations with Superconducting Circuits

Quantum simulations consist in the intentional reproduction of physical or unphysical models into another more controllable quantum system. Beyond establishing communication vessels between unconnected fields, they promise to solve complex problems which may be considered as intractable for classical computers. From a historic perspective, two independent approaches have been pursued, namely, digital and analog quantum simulations. The former usually provide universality and flexibility, while the latter allows for better scalability. Here, we review recent literature merging both paradigms in the context of superconducting circuits, yielding: digital-analog quantum simulations. In this manner, we aim at getting the best of both approaches in the most advanced quantum platform involving superconducting qubits and microwave transmission lines. The discussed merge of quantum simulation concepts, digital and analog, may open the possibility in the near future for outperforming classical computers in relevant problems, enabling the reach of a quantum advantage.Comment: Review article, 26 pages, 4 figure

Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem

The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial optimization problem in logistics. Quantum and hybrid quantum-classical algorithms are expected to show an advantage over their classical counterparts in obtaining approximate solutions for optimization problems. We have recently proposed a hybrid approach to the one dimensional BPP in which a quantum annealing subroutine is employed to sample feasible solutions for single containers. From this reduced search space, a classical optimization subroutine can find the solution to the problem. With the aim of going a step further in the evaluation of our subroutine, in this paper we compare the performance of our procedure with other classical approaches. Concretely we test a random sampling and a random-walk-based heuristic. Employing a benchmark comprising 18 instances, we show that the quantum approach lacks the stagnation behaviour that slows down the classical algorithms. Based on this, we conclude that the quantum strategy can be employed jointly with the random walk to obtain a full sample of feasible solutions in fewer iterations. This work improves our intuition about the benefits of employing the scarce quantum resources to improve the results of a diminishingly efficient classical strategy.Comment: 8 pages, 2 figures, submitted to the IEEE Symposium Series On Computational Intelligence 202

Density-aware person detection and tracking in crowds

International audienceWe address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections jointly. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds. We validate our approach on a challenging video dataset of crowded scenes

Data-driven Crowd Analysis in Videos

International audienceIn this work we present a new crowd analysis algorithm powered by behavior priors that are learned on a large database of crowd videos gathered from the Internet. The algorithm works by first learning a set of crowd behavior priors off-line. During testing, crowd patches are matched to the database and behavior priors are transferred. We adhere to the insight that despite the fact that the entire space of possible crowd behaviors is infinite, the space of distinguishable crowd motion patterns may not be all that large. For many individuals in a crowd, we are able to find analogous crowd patches in our database which contain similar patterns of behavior that can effectively act as priors to constrain the difficult task of tracking an individual in a crowd. Our algorithm is data-driven and, unlike some crowd characterization methods, does not require us to have seen the test video beforehand. It performs like state-ofthe-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individual behaves in an unusual way

Efficient amplitude encoding of polynomial functions into quantum computers

Loading functions into quantum computers represents an essential step in several quantum algorithms, such as in the resolution of partial derivative equations. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions. The first one relies on the matrix product state representation, where we study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines, initially we encode the linear function into the quantum registers with a swallow sequence of multi-controlled gates that loads its Hadamard-Walsh series expansion, followed by the inverse discrete Hadamard-Walsh transform. Then, we use this construction as a building block to achieve a $\mathcal{O}(n)$ block encoding of the amplitudes corresponding to the linear function and apply the quantum singular value transformation that implements the corresponding polynomial transformation to the block encoding of the amplitudes. Additionally, we explore how truncating the Hadamard-Walsh series of the linear function affects the final fidelity of the target state, reporting high fidelities with small resources

Digital-Analog Quantum Computation

Digital quantum computing paradigm offers highly-desirable features such as universality, scalability, and quantum error correction. However, physical resource requirements to implement useful error-corrected quantum algorithms are prohibitive in the current era of NISQ devices. As an alternative path to performing universal quantum computation, within the NISQ era limitations, we propose to merge digital single-qubit operations with analog multi-qubit entangling blocks in an approach we call digital-analog quantum computing (DAQC). Along these lines, although the techniques may be extended to any resource, we propose to use unitaries generated by the ubiquitous Ising Hamiltonian for the analog entangling block and we prove its universal character. We construct explicit DAQC protocols for efficient simulations of arbitrary inhomogeneous Ising, two-body, and $M$-body spin Hamiltonian dynamics by means of single-qubit gates and a fixed homogeneous Ising Hamiltonian. Additionally, we compare a sequential approach where the interactions are switched on and off (stepwise~DAQC) with an always-on multi-qubit interaction interspersed by fast single-qubit pulses (banged DAQC). Finally, we perform numerical tests comparing purely digital schemes with DAQC protocols, showing a remarkably better performance of the latter. The proposed DAQC approach combines the robustness of analog quantum computing with the flexibility of digital methods

Large-scale 3D printing with cable-driven parallel robots

Gantry robots and anthropomorphic arms of various sizes have already been studied and, while they are in use in some parts of the world for automated construction, a new kind of wide workspace machinery, cable-driven parallel robots (CDPR), has emerged. These robots are capable of automated movement in a very wide workspace, using cables reeled in and out by winches as actuation members, the other elements being easily stacked for easy relocation and reconfiguration, which is critical for on-site construction. The motivation of this paper is to showcase the potential of a CDPR operating solely on motor position sensors and showing limited collisions from the cables for large-scale applications in the building industry relevant for additive manufacturing, without risk of collisions between the cables and the building. The combination of the Cogiro CDPR (Tecnalia, LIRMM-CNRS 2010) with the extruder and material of the Pylos project (IAAC 2013) opens the opportunity to a 3D printing machine with a workspace of 13.6 × 9.4 × 3.3 m. The design patterns for printing on such a large scale are disclosed, as well as the modifications that were necessary for both the Cogiro robot and Pylos extruder and material. Two prints, with different patterns, have been achieved with the Pylos extruder mounted on Cogiro: the first spanning 3.5 m in length, the second, reaching a height of 0.86 m. Based on this initial experiment, plans for building larger parts and buildings are discussed, as well as other possible applications for CDPRs in construction, such as the manipulation of assembly processes (windows, lintels, beams, floor elements, curtain wall modules, etc.) or brick laying

Impact of Early Non-Invasive Ventilation in Amyotrophic Lateral Sclerosis: A multicenter Randomized Controlled Trial

Respiratory complications; Respiratory insufficiency; SurvivalComplicaciones respiratorias; Insuficiencia respiratoria; SupervivenciaComplicacions respiratòries; Insuficiència respiratòria; SupervivènciaBackground and objective: Forced vital capacity (FVC) less than 50% of predicted is one of the main parameters used for Non-Invasive Ventilation (NIV) initiation in Amyotrophic Lateral Sclerosis (ALS). Recent studies suggest that higher values of FVC could be considered as a threshold. The aim of this study is to evaluate whether early use of NIV improves the prognosis of ALS patients compared with standard initiation. Methods: This is a randomized, parallel, multicenter, open-label, controlled clinical trial, with recruitment at the ALS outpatient multidisciplinary units of six Spanish hospitals. Patients were included when their FVC reached the 75% threshold and were randomized by computer, stratifying by center in an allocation ratio of 1:1 to Early NIV (FVC below 75%) or Standard NIV (FVC below 50%) initiation. The primary outcome was time to death or tracheostomy. Trial registration number ClinicalTrials.gov: NCT01641965. Results: Between May 2012 and June 2014, 42 patients were randomized to two groups, 20 to Early NIV and 22 to Standard NIV initiation. We found differences in survival in favor of the intervention group: an incidence of mortality (2.68 [1.87–5.50] vs. 3.33 [1.34–4.80] person-months) and a median survival (25.2 vs. 19.4 months), although without reaching statistical significance (p = 0.267). Conclusions: This trial did not reach the primary endpoint of survival; nevertheless, it is the first Randomized Controlled Trial (RCT) to demonstrate the benefits of early NIV in slowing the decline of respiratory muscle strength and reducing adverse events. Although not all the results reached statistical significance, all the analyzed data favor early NIV. In addition, this study demonstrates good tolerance and compliance with early NIV without quality of sleep impairment. These data reinforce the early respiratory evaluation of ALS patients and NIV initiation with an FVC of around 75%.This study received Health Research Fund (FIS) from Carlos III Health Institute (ISCIII), and a grant from Catalan Society of Pneumology (SOCAP)

Microstructural Evolution as a Function of Increasing Aluminum Content in Novel Lightweight Cast Irons

In the context of the development of new lightweight materials, Al-alloyed cast irons have a great potential for reducing the weight of the different part of the vehicles in the transport industry. The correlation of the amount of Al and its effect in the microstructure of cast irons is not completely well established as it is affected by many factors such as chemical composition, cooling rate, etc. In this work, four novel lightweight cast irons were developed with different amounts of Al (from 0 wt. % to 15 wt. %). The alloys were manufactured by an easily scalable and affordable gravity casting process in an induction furnace, and casted in a resin-bonded sand mold. The microstructural evolution as a function of increasing Al content by different microstructural characterization techniques was studied. The hardness of the cast irons was measured by the Vickers indentation test and correlated with the previously characterized microstructures. In general, the microstructural evolution shows that the perlite content decrease with the increment of wt. % of Al. The opposite occurs with the ferrite content. In the case of graphite, a slight increment occurs with 2 wt. % of Al, but a great decrease occurs until 15 wt. % of Al. The addition of Al promotes the stabilization of ferrite in the studied alloys. The hardness obtained varied from 235 HV and 363 HV in function of the Al content. The addition of Al increases the hardness of the studied cast irons, but not gradually. The alloy with the highest hardness is the alloy containing 7 wt. % Al, which is correlated with the formation of kappa-carbides and finer perlite.This work has been partially funded by the Basque Government through the project Elkartek LION: KK-2019/0004