Computational Platform Research Articles

Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization

The protein design problem involves finding polypeptide sequences folding into a given three-dimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine-learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design. Published by the American Physical Society 2024

Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks.

Pursuing high-fidelity control of spin qubits in natural Si/SiGe quantum dot

Electron spins in silicon quantum dots are a promising platform for fault-tolerant quantum computing. Low-frequency noise, including nuclear spin fluctuations and charge noise, is a primary factor limiting gate fidelities. Suppressing this noise is crucial for high-fidelity qubit operations. Here, we report on a two-qubit quantum device in natural silicon with universal qubit control, designed to investigate the upper limits of gate fidelities in a non-purified Si/SiGe quantum dot device. By employing advanced device structures, qubit manipulation techniques, and optimization methods, we have achieved single-qubit gate fidelities exceeding 99% and a two-qubit controlled-Z (CZ) gate fidelity of 91%. Decoupled CZ gates are used to prepare Bell states with an average fidelity of 91%, typically exceeding previously reported values in natural silicon devices. These results underscore that even natural silicon has the potential to achieve high-fidelity gate operations, particularly with further optimization methods to suppress low-frequency noise.

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique, OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for effective structure-based drug design, with potentially broad applications in drug discovery.

Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems

The fusion energy research field presents many intricate challenges that require resolution. Many diagnostic systems employed in experiments are approaching the limits of current technology. Implementing efficient measurements requires using an appropriate set of tools to facilitate the optimal utilization of hardware. Fusion energy measurements must provide low latency processing with the capacity for future improvements and the ability to handle complex data flows efficiently. The presented work addresses these requirements and describes the implementation of a high-performance, low-latency software platform with convenient development for soft X-ray (SXR) plasma impurities emission tracing—the Asynchronous Complex Computation Platform (AC2P). This article presents the architectural design, implementation details, and performance and latency measurements based on the raw data acquired from the WEST tokamak and laboratory tests. AC2P provides the tools to develop low-latency, multi-core, multi-device complex data flow graph scale-up solutions for measuring impurities in hot plasmas. The system has been designed to operate online during experiments, calculate the energy distribution, position and occurrence time of SXR photons, monitor the data stream’s quality and archive any abnormalities for subsequent offline verification and algorithm improvement. This article presents AC2P, which operates as part of the SXR measurement system on the WEST tokamak.

Improving heat transfer in an air-cooled engine by redesigning the fins

Heat transfer modelling and simulation were carried out in a single-cylinder, four-stroke, air-cooled engine to evaluate the heat transfer rate of the engine block. The modelling studies of cylinders with different numbers of fins and different geometry were performed using the SolidWorks computer platform. The tested components were made of 6063-T6 aluminium alloy castings. The simulation concerned different numbers of fins as well as changing the geometry of fins with circular and rectangular perforations. The results of the studies showed the possibility of improving the power to mass ratio for cylinder efficiency and heat transfer rate. It was shown that a large number of fins leads to an increased heat transfer rate, but it affects the overall engine efficiency due to the increase in the total engine mass. Circular perforation is a better design solution than rectangular perforated fins with the same cross-section. Circular perforation provides a lower engine cylinder mass and gives more than 4 % better heat transfer rate. The perforation size was tested using circular perforations with a diameter of 7.14 mm, 8.5 mm and 10 mm. With a 7.14 mm diameter perforation the heat transfer rate increases slightly compared to the other tested ones, while a 10 mm diameter perforation provides the best mass reduction.

Benchmarking Digital Quantum Simulations Above Hundreds of Qubits Using Quantum Critical Dynamics

The real-time simulation of large many-body quantum systems is a formidable task, that may only be achievable with a genuine quantum computational platform. Currently, quantum hardware with a number of qubits sufficient to make classical emulation challenging is available. This condition is necessary for the pursuit of a so-called quantum advantage, but it also makes verifying the results very difficult. In this paper, we flip the perspective and utilize known theoretical results about many-body quantum critical dynamics to qualitatively benchmark digital quantum hardware and various error mitigation techniques. In particular, we benchmark against known universal scaling laws in the Hamiltonian simulation of a time-dependent transverse-field Ising Hamiltonian of up to 133 qubits. Incorporating only basic error mitigation and suppression methods, our study shows reliable control up to a two-qubit gate depth of 28, featuring a maximum of 1396 two-qubit gates, before noise becomes prevalent. These results are transferable to applications such as Hamiltonian simulation, variational algorithms, optimization, or quantum machine learning. We demonstrate this on the example of digitized quantum annealing for optimization and identify an optimal working point in terms of both circuit depth and time step on a 133-site optimization problem. Published by the American Physical Society 2024

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations.

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

FPGA implementation of an optimized neural network for CFD acceleration

In this work, an evaluation of FPGAs as the central computation platform in domain-specific AI-accelerated CFD simulations is performed. This evaluation is performed in three categories: power efficiency, speed, and accuracy. The specific domain in the study is the FDA nozzle benchmark, which is simulated using SimpleFoam, a laminar solver that is a component of the OpenFOAM CFD toolbox. The proposed AI model is a low-parameter feed-forward neural network with three fully connected layers, trained using steady-state solutions distinguished by various Reynolds numbers, all of which are computed by the OpenFOAM framework. The proposed model can then generate the steady-state CFD simulation result given the initial few iterations generated by the solver. Moreover, this paper introduces a hardware implementation for inference of the simulation results using an SoC chip with minimal hardware resource utilization. The suggested hardware design is developed from scratch for Zynq-7000 System-on-Chip, using only VHDL, and requiring no dependencies on third-party commercial AI frameworks or costly FPGA boards designed for AI-related applications. The proposed workflow in the test case study achieves a 98% reduction in simulation time while maintaining relatively high accuracy and a 95.6% reduction in energy consumption compared with the regular CFD workflow.

Defect dynamics modeling of mesoscale plasticity

The collective motion of defects and their interaction are the basic building blocks for plastic deformation and corresponding mechanical behaviors of crystalline metals. Especially, dislocations among various defects are the “carrier” of plastic deformation in many crystalline materials, particularly ductile materials. To get a fundamental understanding of plastic deformation mechanisms, it calls for an integrated computational platform to simultaneously capture detailed defects characteristics across several length scales together with corresponding macroscopic mechanical response. In this paper, we present a three-dimensional mesoscale defect dynamics model to directly couple the three dimensional discrete dislocation dynamics model with continuum finite element method, aiming at capturing both size dependent plasticity at micron-, and submicron scale and constitutive behaviors at larger scales where such size-dependence disappear. Using non-singular dislocation theories, our model could accurately consider both short- and long-range elastic interactions between multiple dislocation segments with even higher computational efficiency than traditional dislocation dynamics simulations, together with the careful consideration of crystal/material rotation in the coupled framework. In addition, our model could directly model dislocation nucleation from stress concentrators such as a void, crack and indentor tip, which could allow us to investigate various defects’ motion and their mutual interactions, predicting macroscopic mechanical response of complex structures. The developed concurrently coupled model could also consider multiphysical phenomena by solving coupled governing equations in finite element framework, which could shed light on complex defect behaviors under various physical environments.

Universal control of four singlet-triplet qubits.

The coherent control of interacting spins in semiconductor quantum dots is of strong interest for quantum information processing and for studying quantum magnetism from the bottom up. Here we present a 2 × 4 germanium quantum dot array with full and controllable interactions between nearest-neighbour spins. As a demonstration of the level of control, we define four singlet-triplet qubits in this system and show two-axis single-qubit control of each qubit and SWAP-style two-qubit gates between all neighbouring qubit pairs, yielding average single-qubit gate fidelities of 99.49(8)-99.84(1)% and Bell state fidelities of 73(1)-90(1)%. Combining these operations, we experimentally implement a circuit designed to generate and distribute entanglement across the array. A remote Bell state with a fidelity of 75(2)% and concurrence of 22(4)% is achieved. These results highlight the potential of singlet-triplet qubits as a competing platform for quantum computing and indicate that scaling up the control of quantum dot spins in extended bilinear arrays can be feasible.

ExaWorks software development kit: a robust and scalable collection of interoperable workflows technologies

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

Inclusive Life Projects for people with Serious Mental Disorder and/or addictions

Abstract Project “Inclusive Life Projects” offers comprehensive and community care to people with SMD/A within projects of autonomous living, which provide a concrete and real response to their needs, with the ultimate objective of avoiding the risk of institutionalization. Currently, 26 actions have been designed and are being implemented within JA ImpleMENTAL pilot in five municipalities in the Region of Murcia: Caravaca, Cartagena, Lorca. Molina de Segura and Yecla with a participation of 50 people with SMD/A. The following elements are selected from the Belgian good practice: • Creation of local intersectoral networks promoting person-centered care process; • Personalization of care through the Individualized Care Plan, responding to the needs, and desires of people with SMI/A, activating the appropriate support; • Professional case manager. It generates the well-being of people on an empathy and trust basis and articulates coordination so that the agreed Plan can be developed as planned. A strategic and support coordination tool for action and facilitation of institutional support, the Social and Health Coordination Protocol for people with SMI/A, has been developed. The protocol facilitates the creation of coordination structures, among them the Socio-Health Coordination Base Teams stand out, teams made up of professionals from the different agents involved, multi-professional and intersectoral, (Mental Health, Primary Health Care, Social Services, Social Initiative) taking into account the needs of the person. It also serves to share information, detect training needs, influence resources, etc. PETRA, a computer platform, has been developed for sharing information between the systems involved, especially the health system and the social services system, both primary care, hospital care and specialized care. It allows the exchange of information, in real time, between professionals, the planning of actions, and the design of indicators and control and evaluation processes.

Cell-nanoparticle stickiness and dose delivery in a multi-model in silico platform: DosiGUI

BackgroundIt is well-known that nanoparticles sediment, diffuse and aggregate when dispersed in a fluid. Once they approach a cell monolayer, depending on the affinity or “stickiness” between cells and nanoparticles, they may adsorb instantaneously, settle slowly – in a time- and concentration-dependent manner - or even encounter steric hindrance and rebound. Therefore, the dose perceived by cells in culture may not necessarily be that initially administered. Methods for quantifying delivered dose are difficult to implement, as they require precise characterization of nanoparticles and exposure scenarios, as well as complex mathematical operations to handle the equations governing the system dynamics. Here we present a pipeline and a graphical user interface, DosiGUI, for application to the accurate nano-dosimetry of engineered nanoparticles on cell monolayers, which also includes methods for determining the parameters characterising nanoparticle-cell stickiness.ResultsWe evaluated the stickiness for 3 industrial nanoparticles (TiO2 – NM-105, CeO2 – NM-212 and BaSO4 – NM-220) administered to 3 cell lines (HepG2, A549 and Caco-2) and subsequently estimated corresponding delivered doses. Our results confirm that stickiness is a function of both nanoparticle and cell type, with the stickiest combination being BaSO4 and Caco-2 cells. The results also underline that accurate estimations of the delivered dose cannot prescind from a rigorous evaluation of the affinity between the cell type and nanoparticle under investigation.ConclusionAccurate nanoparticle dose estimation in vitro is crucial for in vivo extrapolation, allowing for their safe use in medical and other applications. This study provides a computational platform – DosiGUI – for more reliable dose-response characterization. It also highlights the importance of cell-nanoparticle stickiness for better risk assessment of engineered nanomaterials.

Simulation of Responsive Envelopes in Current and Future Climate Scenarios: A New Interactive Computational Platform for Energy Analyses

Despite the strong interest concerning the responsive façades, today there are still few built examples and few tools to assess their benefits due to the complex description of the phenomenon. Energy simulations should consider the interactions between a time-varying environment and an environment-dependent envelope, increasing the intricacy of the problem; moreover, these strong environment–envelope interlinkages increase the importance of the location and climate scenarios considered. The aim of this study is to provide a tool to easily model these phenomena in different geographical and climate contexts. For this purpose, an innovative interactive computational platform (ICP) was developed based on EnergyPlus as a simulation engine, Python as a simulation manager, and Grasshopper as a user interface. Thanks to a single user-friendly environment, the users can simply select the climate scenario, the location, the responsive technology, and its main properties to set and run the dynamic energy simulation. After an overview of the current state of the art, this study provides a description of the structure and workflow adopted for developing this platform and details regarding its functioning and input management. Finally, the platform was tested to run an evolutionary optimization of an electrochromic window control strategy in different climate scenarios.

Design and implementation of the dual-center programming platform for ternary optical computer and electronic computer

In order to utilize the advantages of optical computing and promote the application and popularization of Ternary Optical Computer (TOC), this paper proposes a dual-center programming model consisting of electronic processor and optical processor, presents the theory and technologies of the dual-center model in detail, and for the first time explains the SAN ZHI GUANG (SZG) file chain technology, gives the implementation method of the dual-center model. The model tries to effectively manage the resources of optical processor and solve the problem of the distance and network connection mode of using the TOC. Experimental results show that the dual-center model is correct and the implementation method is feasible. It can improve the usability of the TOC and further simplify the TOC programming process, and makes common users apply the TOC and electronic computer to work cooperatively for the same task.

Scalable Architecture for Trapped-Ion Quantum Computing Using rf Traps and Dynamic Optical Potentials

Qubits based on ions trapped in linear radio-frequency traps form a successful platform for quantum computing, due to their high fidelity of operations, all-to-all connectivity, and degree of local control. In principle, there is no fundamental limit to the number of ion-based qubits that can be confined in a single 1D register. However, in practice, there are two main issues associated with long trapped-ion crystals, that stem from the “softening” of their modes of motion, upon scaling up: high heating rates of the ions’ motion and a dense motional spectrum; both impede the performance of high-fidelity qubit operations. Here, we propose a holistic, scalable architecture for quantum computing with large ion crystals that overcomes these issues. Our method relies on dynamically operated optical potentials that instantaneously segment the ion crystal into cells of a manageable size. We show that these cells behave as nearly independent quantum registers, allowing for parallel entangling gates on all cells. The ability to reconfigure the optical potentials guarantees connectivity across the full ion crystal and also enables efficient midcircuit measurements. We study the implementation of large-scale parallel multiqubit entangling gates that operate simultaneously on all cells and present a protocol to compensate for crosstalk errors, enabling full-scale usage of an extensively large register. We illustrate that this architecture is advantageous both for fault-tolerant digital quantum computation and for analog quantum simulations. Published by the American Physical Society 2024

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes.

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs. We reconstruct these motifs with a new computational platform - Joint Protein-Ligand Embedding (JPLE) - which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of known RBP motifs, expanding the motif repertoire across all major eukaryotic clades, and assigning motifs to the majority of human RBPs. EuPRI drastically improves knowledge of RBP motifs in flowering plants. For example, it increases the number of Arabidopsis thaliana RBP motifs 7-fold, from 14 to 105. EuPRI also has broad utility for inferring post-transcriptional function and evolutionary relationships. We demonstrate this by predicting a role for 12 Arabidopsis thaliana RBPs in RNA stability and identifying rapid and recent evolution of post-transcriptional regulatory networks in worms and plants. In contrast, the vertebrate RNA motif set has remained relatively stable after its drastic expansion between the metazoan and vertebrate ancestors. EuPRI represents a powerful resource for the study of gene regulation across eukaryotes.

Variational benchmarks for quantum many-body problems.

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods toward a quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible.

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification.

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of <7% and a very large dynamic range, covering cTnI concentrations over 6 orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing.