Scalable Computation Research Articles

Simulations of superconducting quantum gates by digital flux tuner for qubits

Abstract The interconnection bottleneck caused by limitations of cable number, inner space and cooling power of dilution refrigerators has been an outstanding challenge for building scalable superconducting quantum computers with the increasing number of qubits in quantum processors. To surmount such an obstacle, it is desirable to integrate qubits with quantum–classical interface (QCI) circuits based on rapid single flux quantum (RSFQ) circuits. In this work, a digital flux tuner for qubits (DFTQ) is proposed for manipulating flux of qubits as a crucial part of the interface circuit. A schematic diagram of the DFTQ is presented, consisting of a coarse tuning unit and a fine-tuning unit for providing magnetic flux with different precision to qubits. The method of using DFTQ to provide flux for gate operations is discussed from the optimization of circuit design and input signal. To verify the effectiveness of the method, simulations of a single DFTQ and quantum gates including a Z gate and an iSWAP gate with DFTQs are performed for flux-tunable transmons. The quantum process tomography corresponding to the two gates is also carried out to analyze the sources of gate error. The results of tomography show that the gate fidelities independent of the initial states of the Z gate and the iSWAP gate are 99.935% and 99.676%, respectively. With DFTQs inside, the QCI would be a powerful tool for building large-scale quantum computers.

Tensor Flow-powered Spam Email Filtering: An Evaluation of Performance and Robustness

Spam emails continue to pose significant challenges in email communication, requiring robust and adaptive filtering mechanisms to safeguard users and organizations. Leveraging the capabilities of machine learning and deep learning, this paper presents an evaluation of a spam email filtering system powered by Tensor Flow. The system's architecture is designed to utilize deep neural networks for feature extraction and classification, enabling flexibility and scalability in handling diverse spam tactics. We assess the system's performance in accurately distinguishing between spam and legitimate emails, evaluating metrics such as precision, recall, and F1-score. Additionally, we analyze the system's resilience against adversarial attacks and its ability to adapt to evolving spam techniques. Comparative analysis with traditional spam filtering techniques highlights the superiority of deep learning-based approaches. Practical considerations such as computational efficiency and scalability are also addressed, ensuring real-time responsiveness in processing vast volumes of emails. Through comprehensive experimentation and benchmarking, this paper contributes to the advancement of spam email filtering, guiding the development of more effective and efficient solutions for enhancing email security and user experience.

Scalable computation of energy functions for nonlinear balanced truncation

Nonlinear balanced truncation is a model order reduction technique that reduces the dimension of nonlinear systems in a manner that accounts for either open- or closed-loop observability and controllability aspects of the system. A computational challenges that has so far prevented its deployment on large-scale systems is that the energy functions required for characterization of controllability and observability are solutions of various high-dimensional Hamilton–Jacobi–(Bellman) equations, which are computationally intractable in high dimensions. This work proposes a unifying and scalable approach to this challenge by considering a Taylor-series-based approximation to solve a class of parametrized Hamilton–Jacobi–Bellman equations that are at the core of nonlinear balancing. The value of a formulation parameter provides either open-loop balancing or a variety of closed-loop balancing options. To solve for the coefficients of Taylor-series approximations to the energy functions, the presented method derives a linear tensor system and heavily utilizes it to numerically solve structured linear systems with billions of unknowns. The strength and scalability of the algorithm is demonstrated on two semi-discretized partial differential equations, namely the Burgers and the Kuramoto–Sivashinsky equations.

Tucker Tensor Approach for Accelerating Fock Exchange Computations in a Real-Space Finite-Element Discretization of Generalized Kohn-Sham Density Functional Theory.

The evaluation of Fock exchange is often the computationally most expensive part of hybrid functional density functional theory calculations in a systematically improvable, complete basis. In this work, we employ a Tucker tensor based approach that substantially accelerates the evaluation of the action of Fock exchange by transforming three-dimensional convolutional integrals into a tensor product of one-dimensional convolution integrals. Our numerical implementation uses a parallelization strategy that balances the memory and communication bottlenecks, alongside overlapping compute and communication operations to enhance computational efficiency and parallel scalability. The accuracy and computational efficiency are demonstrated on various systems, including Pt clusters of various sizes and a TiO2 cluster with 3684 electrons.

Scalable computation of anisotropic vibrations for large macromolecular assemblies

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

Zero-Trust Cryptographic Protocols and Differential Privacy Techniques for Scalable Secure Multi-Party Computation in Big Data Analytics

This research explores the integration of zero-trust cryptographic protocols and differential privacy techniques to establish scalable secure multi-party computation in the context of big data analytics. The study delves into the challenges of collaborative data processing and presents a comprehensive framework that addresses the intricate balance between security, scalability, and privacy. The framework focuses on zero-trust cryptographic protocols, advocating for a fundamental shift in trust assumptions within distributed systems. Differential privacy techniques are then seamlessly integrated to preserve individual privacy during collaborative data analytics. This model employs a layered approach and distributed architecture and leverages serverless and edge computing fusion to enhance scalability and responsiveness in dynamic big data environments. This also explores the optimization of computational resources and real-time processing capabilities through serverless and edge computing fusion. A distributed architecture facilitates efficient collaboration across multiple parties, allowing for seamless data integration, preprocessing, analytics, and visualization. Privacy preservation takes centre stage in the big data privacy component of the framework. Context-aware attribute analysis, distributed federated learning nodes, and Attribute-Based Access Control (ABAC) with cryptographic enforcement are introduced to ensure fine-grained access control, contextual understanding of attributes, and collaborative model training without compromising sensitive information. Smart Multi-Party Computation Protocols (SMPCP) further enhance security, enabling joint computation of functions over private inputs while ensuring the integrity and immutability of data transactions. In essence, the achieved results manifest a paradigm shift where the layered approach, distributed architecture, and advanced privacy techniques converge to heighten data security, drive efficient computation, and robustly preserve privacy in the expansive landscape of big data analytics. Fault tolerance and resource utilization exhibit significant advancements, with fault tolerance experiencing a 10% boost and resource utilization optimizing by 12%. These enhancements underscore the robustness and efficiency of the system's design, ensuring resilience and optimized resource allocation.

Upgrading strategies for long-haul quantum key distribution networks

Quantum key distribution (QKD) has become a promising option for transmitting sensitive data due to the increased maturity of QKD devices and the threat scalable quantum computers imposes on asymmetric public-key cryptosystems. By utilizing existing infrastructure, e.g., amplifier huts and dark fibers, the cost and complexity of deploying QKD networks (QKDNs) can be reduced. In this study, we develop a topology optimization algorithm that minimizes the cost of the QKDN deployment and maximizes the key capacity between any pair of nodes. We present three deployment upgrade strategies for facilitating the encryption of increasing throughput of QKD-secured optical transport networks and enabling a pay-as-you-grow approach. Comparing different strategies in a capacity-planning study allows operators to assess the scalability of deployments and upgrades. Depending on the availability requirements, our results compare the cost of protection measures. We confirm that adding trusted nodes is the most cost-efficient upgrading strategy based on our analysis.

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Big data mining in English teaching revolutionizes language instruction by leveraging vast amounts of data to personalize and optimize learning experiences. This approach utilizes sophisticated algorithms to analyze learners' language usage patterns, comprehension levels, and areas of difficulty. This paper presents a novel Quality Improvement Model of English Teaching (QIMET) tailored for universities, integrating big data mining techniques with a Stacked Hashing Edge Computing Model (SHECM). Recognizing the importance of enhancing English language proficiency among university students, particularly in an increasingly globalized educational landscape, this research aims to optimize English teaching methodologies through advanced computational approaches. The proposed QIMET framework leverages big data mining to analyze extensive datasets of student performance, language usage patterns, and instructional effectiveness. By extracting valuable insights from these datasets, educators can identify areas for improvement, tailor teaching materials, and personalize learning experiences to meet individual student needs. Simulation analysis of Across a dataset of 500 university students enrolled in English language courses, QIMET achieves an average improvement of 30% in language proficiency scores compared to traditional teaching methods. Moreover, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 25%, grammar comprehension by 20%, and communication skills by 35%. The integration of the SHECM enhances the computational efficiency and scalability of the QIMET framework. Real-time data processing and analysis enable educators to make timely interventions and adjustments to teaching strategies, resulting in more responsive and effective English language instruction.

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

Electric field dependence of spin qubit in a Si-MOS quantum dot

Abstract Valley, the intrinsic feature of silicon, is an inescapable subject in silicon-based quantum computing. At the spin–valley hotspot, both Rabi frequency and state relaxation rate are significantly enhanced. With protection against charge noise, the valley degree of freedom is also conceived to encode a qubit to realize noise-resistant quantum computing. Here, based on the spin qubit composed of one or three electrons, we characterize the intrinsic properties of valley in an isotopically enriched silicon quantum dot (QD) device. For one-electron qubit, we measure two electric-dipole spin resonance (EDSR) signals which are attributed to partial occupation of two valley states. The resonance frequencies of two EDSR signals have opposite electric field dependences. Moreover, we characterize the electric field dependence of the upper valley state based on three-electron qubit experiments. The difference of electric field dependences of the two valleys is 52.02 MHz/V, which is beneficial for tuning qubit frequency to meet different experimental requirements. As an extension of electrical control spin qubits, the opposite electric field dependence is crucial for qubit addressability, individual single-qubit control and two-qubit gate approaches in scalable quantum computing.

High-fidelity spin qubit operation and algorithmic initialization above 1 K

The encoding of qubits in semiconductor spin carriers has been recognized as a promising approach to a commercial quantum computer that can be lithographically produced and integrated at scale1–10. However, the operation of the large number of qubits required for advantageous quantum applications11–13 will produce a thermal load exceeding the available cooling power of cryostats at millikelvin temperatures. As the scale-up accelerates, it becomes imperative to establish fault-tolerant operation above 1 K, at which the cooling power is orders of magnitude higher14–18. Here we tune up and operate spin qubits in silicon above 1 K, with fidelities in the range required for fault-tolerant operations at these temperatures19–21. We design an algorithmic initialization protocol to prepare a pure two-qubit state even when the thermal energy is substantially above the qubit energies and incorporate radiofrequency readout to achieve fidelities up to 99.34% for both readout and initialization. We also demonstrate single-qubit Clifford gate fidelities up to 99.85% and a two-qubit gate fidelity of 98.92%. These advances overcome the fundamental limitation that the thermal energy must be well below the qubit energies for the high-fidelity operation to be possible, surmounting a main obstacle in the pathway to scalable and fault-tolerant quantum computation.

XLB: A differentiable massively parallel lattice Boltzmann library in Python

The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid dynamics problems due to its algorithmic potential for computational scalability. We introduce XLB library, a Python-based differentiable LBM library based on the JAX platform. The architecture of XLB is predicated upon ensuring accessibility, extensibility, and computational performance, enabling scaling effectively across CPU, TPU, multi-GPU, and distributed multi-GPU or TPU systems. The library can be readily augmented with novel boundary conditions, collision models, or multi-physics simulation capabilities. XLB's differentiability and data structure is compatible with the extensive JAX-based machine learning ecosystem, enabling it to address physics-based machine learning, optimization, and inverse problems. XLB has been successfully scaled to handle simulations with billions of cells, achieving giga-scale lattice updates per second. XLB is released under the permissive Apache-2.0 license and is available on GitHub at https://github.com/Autodesk/XLB. Program summaryProgram Title: XLBCPC Library link to program files:https://doi.org/10.17632/n25h65vw59.1Developer's repository link:https://github.com/Autodesk/XLBLicensing provisions: Apache-2.0Programming language: PythonNature of problem: XLB is a scalable Lattice Boltzmann simulation library based on JAX. It is designed to be accessible, extensible, and high performance. The library can be readily augmented with novel boundary conditions, collision models, or simulation capabilities. Moreover, XLB is differentiable, offering the unique advantage of integration with JAX's extensive machine learning ecosystem, and tackling physics-based machine learning, optimization, and inverse problems.Solution method: XLB utilizes the JAX library for just-in-time (JIT) compilation and distributed computing, enabling it to scale effectively across distributed multi-GPU/multi-TPU systems. XLB's programming model combines an object-oriented interface for easy extensibility. The library can be readily augmented with novel boundary conditions, collision models, or other simulation capabilities.

Optimization of algorithmic efficiency in AI: Addressing computational complexity and scalability challenges

Optimization of algorithmic efficiency in AI: Addressing computational complexity and scalability challenges

Scalable Fault-Tolerant Quantum Technologies with Silicon Color Centers

The scaling barriers currently faced by both quantum networking and quantum computing technologies ultimately amount to the same core challenge of distributing high-quality entanglement at scale. In this Perspective, a novel quantum information-processing architecture based on optically active spins in silicon is proposed that offers a combined single technological platform for scalable fault-tolerant quantum computing and networking. The architecture is optimized for overall entanglement distribution and leverages color-center spins in silicon (T centers) for their manufacturability, photonic interface, and high-fidelity information-processing properties. Silicon nanophotonic optical circuits allow for photonic links between T centers, which are networked via telecom-band optical photons in a highly connected graph. This high connectivity unlocks the use of low-overhead quantum error-correcting codes, significantly accelerating the time line for modular scalable fault-tolerant quantum repeaters and quantum processors. Published by the American Physical Society 2024

SPASTC: a Spatial Partitioning Algorithm for Scalable Travel-time Computation

Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster.

Ion trap with in-vacuum high numerical aperture imaging for a dual-species modular quantum computer.

Photonic interconnects between quantum systems will play a central role in both scalable quantum computing and quantum networking. Entanglement of remote qubits via photons has been demonstrated in many platforms; however, improving the rate of entanglement generation will be instrumental for integrating photonic links into modular quantum computers. We present an ion trap system that has the highest reported free-space photon collection efficiency for quantum networking. We use a pair of in-vacuum aspheric lenses, each with a numerical aperture of 0.8, to couple 10(1)% of the 493nm photons emitted from a 138Ba+ ion into single-mode fibers. We also demonstrate that proximal effects of the lenses on the ion position and motion can be mitigated.

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Numerical investigation of flow past a cylinder using cumulant lattice Boltzmann method

This paper presents simulations of flow past a circular cylinder within the subcritical Reynolds number (Re) range from 3900 to 2 × 105, utilizing the parameterized cumulant lattice Boltzmann model. In this study, a three-dimensional characteristic boundary condition for incompressible flow has been integrated into the lattice Boltzmann method at the outflow boundary to minimize spurious reflection. The flow field, wake statistics, hydrodynamic force, and power spectra results of Re = 3900 from the cumulant lattice Boltzmann model are exhaustively compared with the laboratory data and other numerical models. Relative to other numerical models employing turbulence closure, the cumulant lattice Boltzmann simulations demonstrate enhanced agreement with the experimental data even with relatively coarser grid resolution. The resolution-spanning feature for the cumulant lattice Boltzmann model in turbulent flows, without using explicit turbulence model, aligns with the previous benchmark case studies. The stability-preserving regularization process in the present model is analyzed. Results indicate that the influence of the regularization parameter is mitigated with improved grid resolution. A specific regularization parameter for flow around cylinder simulations is recommended. Variations in flow properties and hydrodynamic forces within the subcritical Reynolds number range of 3900 to 2 × 105 are analyzed. The results confirm that the parameterized cumulant lattice Boltzmann model can accurately simulate practical engineering flows, characterized by complex separation and recirculation, within the subcritical range. Moreover, the computational efficiency and parallel scalability are compared with other numerical methods.

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing. Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain. One such technology is single flux quantum (SFQ) logic—a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops. The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains. This work harnesses thermal stochasticity in superconducting synapses to emulate stochasticity in biological synapses in which the synapse probabilistically propagates or blocks incoming spikes. The authors also present neuronal, fan-in, and fan-out circuitry inspired by the literature that seamlessly cascade with the synapses for deep neural network construction. Synapse weights and neuron biases are set with bias current, and the authors propose multiple mechanisms for training the network and storing weights. The network primitives are successfully demonstrated in simulation in the context of a rate-coded multi-layer XOR neural network which achieves a wide classification margin. The proposed methodology is based solely on existing SFQ technology and does not employ unconventional superconductive devices or semiconductor transistors, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.