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Routing classical and quantum information is a fundamental task for most quantum information technologies and processes. Here, we consider information encoded in the position of a quantum walker on a graph, and design an optimal structure to achieve perfect quantum routing exploiting chirality and weighting of the edges. The topology, termed the {\em Lily Graph}, enables perfect (i.e., with fidelity one) and robust routing of classical (localized) or quantum (superposition) states of the walker to $n$ different, orthogonal, spatial regions of the graph, corresponding to the $n$ possible outputs of the device. The routing time is independent of the input signal and the number of outputs, making our scheme a robust and scalable solution for quantum networks., Comment: 5 pages, 3 figures

We examine metrological scenarios where the parameter of interest is encoded onto a quantum state through the action of a noisy quantum gate and investigate the ultimate bound to precision by analyzing the behaviour of the Quantum Fisher Information (QFI). We focus on qubit gates and consider the possibility of employing successive applications of the gate. We go beyond the trivial case of unitary gates and characterize the robustness of the metrological procedure introducing noise in the performed quantum operation, looking at how this affects the QFI at different steps (gate applications). We model the dephasing and tilting noise affecting qubit rotations as classical fluctuations governed by a Von Mises-Fisher distribution. Compared to the noiseless case, in which the QFI grows quadratically with the number of steps, we observe a non monotonic behavior, and the appearance of a maximum in the QFI, which defines the ideal number of steps that should be performed in order to precisely characterize the action of the gate., Comment: 11 pages, 5 figures

We address quantum metrology in critical spin chains with anisotropy and Dzyaloshinskii-Moriya (DM) interaction, and show how local and quasi-local measurements may be exploited to characterize global properties of the systems. In particular, we evaluate the classical (magnetization) and quantum Fisher information of the relevant parameters for the density matrix of a single spin and that of a pair of spins ranging from nearest to sixth-nearest neighbors, to the limiting case of very distant spins. Our results allow us to elucidate the role of the different parameters and to individuate the optimal working regimes for the precise characterization of the system, also clarifying the effects of correlations on the estimation precision., Comment: 12 pages, 10 figures

Discrete-time quantum walk (DTQW) represents a convenient mathematical framework for describing the motion of a particle on a discrete set of positions when this motion is conditioned by the values of certain internal degrees of freedom, which are usually referred to as the {\em coin} of the particle. As such, and owing to the inherent dependence of the position distribution on the coin degrees of freedom, DTQWs naturally emerge as promising candidates for quantum metrology. In this paper, we explore the use of DTQWs as quantum probes in scenarios where the parameter of interest is encoded in the internal degree of freedom of the walker, and investigate the role of the topology of the walker's space on the attainable precision. In particular, we start considering the encoding of the parameter by rotations for a walker on the line, and evaluate the quantum Fisher information (QFI) and the position Fisher information (FI), explicitly determining the optimal initial state in position space that maximizes the QFI across all encoding schemes. This allows us to understand the role of interference in the position space and to introduce an optimal topology, which maximizes the QFI of the coin parameter and makes the position FI equal to the QFI., Comment: 11 pages, 5 figures

We address quantum characterization of anisotropic spin chains in the presence of antisymmetric exchange, and investigate whether the Hamiltonian parameters of the chain may be estimated with precision approaching the ultimate limit imposed by quantum mechanics. At variance with previous approaches, we focus on the information that may be extracted by measuring only two neighbouring spins rather than a global observable on the entire chain. We evaluate the Fisher information (FI) of a two-spin magnetization measure, and the corresponding quantum Fisher information (QFI), for all the relevant parameters, i.e. the spin coupling, the anisotropy, and the Dzyaloshinskii Moriya (DM) parameter. Our results show that the reduced system made of two neighbouring spins may be indeed exploited as a probe to characterize global properties of the entire system. In particular, we find that the ratio between the FI and the QFI is close to unit for a large range of the coupling values. The DM coupling is beneficial for coupling estimation, since it leads to the presence of additional bumps and peaks in the FI and QFI, which are not present in a model that neglects exchange interaction and may be exploited to increase the robustness of the overall estimation procedure. Finally, we address the multiparameter estimation problem, and show that the model is compatible but sloppy, i.e. both the Uhlmann curvature and the determinant of the QFI matrix vanish. Physically, this means that the state of the system actually depends only on a reduced numbers of combinations of parameters, and not on all of them separately., Comment: 10 pages, 7 figures

The optimization of the power consumption of antenna networks is a problem with a potential impact in the field of telecommunications. In this work, we investigate the application of the quantum approximate optimization algorithm (QAOA) and the quantum adiabatic algorithm (QAA), to the solution of a prototypical model in this field. We use statevector emulation in a high-performance computing environment to compare the performance of these two algorithms in terms of solution quality, using selected evaluation metrics. We estimate the circuit depth scaling with the problem size while maintaining a certain level of solution quality, and we extend our analysis up to 31 qubits, which is rarely addressed in the literature. Our calculations show that as the problem size increases, the probability of measuring the exact solution decreases exponentially for both algorithms. This issue is particularly severe when we include constraints in the problem, resulting in full connectivity between the sites. Nonetheless, we observe that the cumulative probability of measuring solutions close to the optimal one remains high also for the largest instances considered in this work. Our findings keep the way open to the application of these algorithms, or variants thereof, to generate suboptimal solutions at scales relevant to industrial use-cases., Comment: 14 pages, 12 Figures

We address metrological problems where the parameter of interest is encoded in the internal degree of freedom of a discrete-time quantum walker, and provide evidence that coin dimensionality is a potential resource to enhance precision. In particular, we consider estimation problems where the coin parameter governs rotations around a given axis and show that the corresponding quantum Fisher information (QFI) may increase with the dimension of the coin. We determine the optimal initial state of the walker to maximize the QFI and discuss whether, and to which extent, precision enhancement may be achieved by measuring only the position of the walker. Finally, we consider Grover-like encoding of the parameter and compare results with those obtained from rotation encoding., Comment: revised version, 14 pages, 5 figures

Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community., Comment: 12 pages, Proceedings of ITADATA22, The 1st Italian Conference on Big Data and Data Science; Published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073), Vol. 3340, pp. 99-110, 2022

Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical performance challenge of FL is operating on datasets not independently and identically distributed (non-IID) among the federation participants. Even though this fragility cannot be eliminated, it can be debunked by a suitable optimization of two hyper-parameters: layer normalization methods and collaboration frequency selection. In this work, we benchmark five different normalization layers for training Neural Networks (NNs), two families of non-IID data skew, and two datasets. Results show that Batch Normalization, widely employed for centralized DL, is not the best choice for FL, whereas Group and Layer Normalization consistently outperform Batch Normalization. Similarly, frequent model aggregation decreases convergence speed and mode quality., Comment: 10 pages, Submitted to IEEE Transactions on Neural Networks and Learning Systems

Coherent transport of an excitation through a network corresponds to continuous-time quantum walk on a graph, and the transport properties of the system may be radically different depending on the graph and on the initial state. The transport efficiency, i.e., the integrated probability of trapping at a certain vertex, is a measure of the success rate of the transfer process. Purely coherent quantum transport is known to be less efficient than the observed excitation transport, e.g., in biological systems, and there is evidence that environmental noise is indeed crucial for excitation transport. At variance with this picture, we here address purely coherent transport on highly symmetric graphs, and show analytically that it is possible to enhance the transport efficiency without environmental noise, i.e., using only a minimal perturbation of the graph. In particular, we show that adding an extra weight to one or two edges, depending on whether the initial state is localized or in a superposition of two vertex states, breaks the inherent symmetries of the graph and may be sufficient to achieve unit transport efficiency. We also briefly discuss the conditions to obtain a null transport efficiency, i.e., to avoid trapping., Comment: 19 pages, 4 figures, accepted version

The social and economic impact of the COVID-19 pandemic demands the reduction of the time required to find a therapeutic cure. In the contest of urgent computing, we re-designed the Exscalate molecular docking platform to benefit from heterogeneous computation nodes and to avoid scaling issues. We deployed the Exscalate platform on two top European supercomputers (CINECA-Marconi100 and ENI-HPC5), with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billions of small molecules and 15 binding-sites of 12 viral proteins of Sars-Cov2. The experiment lasted 60 hours and overall it performed a trillion of evaluations.

Quantum ESPRESSO is an open-source distribution of computer codes for quantum-mechanical materials modeling, based on density-functional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware architectures, from laptops to massively parallel computers, as well as for the breadth of its applications. In this paper we present a motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently hindering the way towards exascale computing.

We address the properties of continuous-time quantum walks with Hamiltonians of the form $\mathcal{H}= L + \lambda L^2$, being $L$ the Laplacian matrix of the underlying graph and being the perturbation $\lambda L^2$ motivated by its potential use to introduce next-nearest-neighbor hopping. We consider cycle, complete, and star graphs because paradigmatic models with low/high connectivity and/or symmetry. First, we investigate the dynamics of an initially localized walker. Then, we devote attention to estimating the perturbation parameter $\lambda$ using only a snapshot of the walker dynamics. Our analysis shows that a walker on a cycle graph is spreading ballistically independently of the perturbation, whereas on complete and star graphs one observes perturbation-dependent revivals and strong localization phenomena. Concerning the estimation of the perturbation, we determine the walker preparations and the simple graphs that maximize the Quantum Fisher Information. We also assess the performance of position measurement, which turns out to be optimal, or nearly optimal, in several situations of interest. Besides fundamental interest, our study may find applications in designing enhanced algorithms on graphs., Comment: 21 pages, 16 figures. Version accepted for publication

The power consumption of supercomputers is a major challenge for system owners, users, and society. It limits the capacity of system installations, it requires large cooling infrastructures, and it is the cause of a large carbon footprint. Reducing power during application execution without changing the application source code or increasing time-to-completion is highly desirable in real-life high-performance computing scenarios. The power management run-time frameworks proposed in the last decade are based on the assumption that the duration of communication and application phases in an MPI application can be predicted and used at run-time to trade-off communication slack with power consumption. In this manuscript, we first show that this assumption is too general and leads to mispredictions, slowing down applications, thereby jeopardizing the claimed benefits. We then propose a new approach based on (i) the separation of communication phases and slack during MPI calls and (ii) a timeout algorithm to cope with the hardware power management latency, which jointly makes it possible to achieve performance-neutral power saving in MPI applications without requiring labor-intensive and risky application source code modifications. We validate our approach in a tier-1 production environment with widely adopted scientific applications. Our approach has a time-to-completion overhead lower than 1%, while it successfully exploits slack in communication phases to achieve an average energy saving of 10%. If we focus on a large-scale application runs, the proposed approach achieves 22% energy saving with an overhead of only 0.4%. With respect to state-of-the-art approaches, COUNTDOWN Slack is the only that always leads to an energy saving with negligible overhead (<3%)., Comment: 13 pages, 4 figures, 3 tables

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analyzed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy tradeoffs. We modeled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock - a state-of-the-art molecular docking application - to validate the proposed approach. We run the enhanced Mini-App on an HPC system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign.

In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using High-Performance Computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms. In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 36% while considering the same computing node.

The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

Power and energy consumption is becoming key challenges to deploy the first exascale supercomputer successfully. Large-scale HPC applications waste a significant amount of power in communication and synchronization-related idle times. However, due to the time scale at which communication happens, transitioning in low power states during communication's idle times may introduce unacceptable overhead in applications' execution time. In this paper, we present COUNTDOWN, a runtime library, supported by a methodology and analysis tool for identifying and automatically reducing the power consumption of the computing elements during communication and synchronization. COUNTDOWN saves energy without imposing significant time-to-completion increase by lowering CPUs power consumption only during idle times for which power state transition overhead are negligible. This is done transparently to the user, without requiring labor-intensive and error-prone application code modifications, nor requiring recompilation of the application. We test our methodology in a production Tier-0 system. For the NAS benchmarks, COUNTDOWN saves between 6% and 50% energy, with a time-to-solution penalty lower than 5%. In a complete production --- Quantum ESPRESSO --- for a 3.5K cores run, COUNTDOWN saves 22.36% energy, with a performance penalty below 3%. Energy saving increases to 37% with a performance penalty of 6.38%, if the application is executed without communication tuning., Comment: 14 pages, 11 figures

Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches. Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement theirs ideas. In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software., Comment: Psi-k highlight September 2017: psi-k.net/dowlnload/highlights/Highlight_137.pdf; J. Phys.: Condens. Matter, accepted