Code Architecture Research Articles

Artificial neural network syndrome decoding on IBM quantum processors

Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and benchmarking of Artificial Neural Network (ANN) decoding on IBM quantum processors. We demonstrate that ANNs can efficiently decode syndrome measurement data from heavy-hexagonal code architecture and apply appropriate corrections to facilitate error protection. The current physical error rates of IBM devices are above the code's threshold and restrict the scope of our ANN decoder for logical error rate suppression. However, our work confirms the applicability of ANN decoding methods of syndrome data retrieved from experimental devices and establishes machine learning as a promising pathway for quantum error correction when quantum devices with below threshold error rates become available in the near future. Published by the American Physical Society 2024

Fast joint parity measurement via collective interactions induced by stimulated emission

Parity detection is essential in quantum error correction. Error syndromes coded in parity are detected routinely by sequential CNOT gates. Here, different from the standard CNOT-gate based scheme, we propose a reliable joint parity measurement (JPM) scheme inspired by stimulated emission. By controlling the collective behavior between data qubits and syndrome qubit, we realize the parity detection and experimentally implement the weight-2 and weight-4 JPM scheme in a tunable coupling superconducting circuit, which shows comparable performance to the CNOT scheme. Moreover, with the aid of the coupling tunability in quantum system, this scheme can be further utilized for specific joint entangling state preparation (JEP) with high fidelity, such as multiqubit entangled state preparation for non-adjacent qubits. This strategy, combined with the superconducting qubit system with tunable couplers, reveals tremendous potential and applications in the surface code architecture without adding extra circuit elements. Besides, the method we develop here can readily be applied in large-scale quantum computation and quantum simulation.

Swift : a modern highly parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications

ABSTRACT Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this paper, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid shared- and distributed-memory task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with ≈300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift.

TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows

Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

The AGORA High-resolution Galaxy Simulations Comparison Project. VI. Similarities and Differences in the Circumgalactic Medium

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

Super-Rays Grouping Scheme and Novel Coding Architecture for Computational Time Reduction of Graph-Based Light Field Coding

Graph-based Light Field coding using the concept of super-rays is powerful to exploit signal redundancy along irregular shapes and achieves good energy compaction, compared to rectangular block -based approaches. However, its main limitation lies in the high time complexity for eigen-decomposition of each super-ray local graph, a high number of which can be found in a Light Field when segmented into super-rays. This paper examines a grouping scheme for super-rays in order to reduce the number of eigen-decomposition times, and proposes a novel coding architecture to handle the signal residual data arising for each super-ray group, as a tradeoff to achieve lower computational time. Experimental results have shown to reduce a considerable amount of decoding time for Light Field scenes, despite having a slight increase in the coding bitrates when compared with the original non-grouping super-ray-based approach. The proposal also remains to have competitive performance in Rate Distortion in comparison to HEVC-based and JPEG Pleno -based methods.

The ALICE Membership system

With over 2000 active members from 174 institutes over 41 countries in the world, the ALICE experiment is one of the 4 large experiments at CERN. With such numerous interactions, the experiment management needs a way to record members’ participation history and their current status, such as employment, institutes, appointments, clusters and funding agencies, as well as to automatically generate the physics paper author list considering the experiment publication policy. The ALICE Glance Membership system handles all these needs and has been active for more than 8 years, helping the collaboration to organize their data, efficiently. In this document, we describe the new ALICE Glance Membership system, which is the result of breaking the monolithic code of the old version into two well-defined environments. The first is a REST API written in Object-Oriented PHP using the Slim framework to expose the data and Doctrine to access and manage an ORACLE database together with a Domain Driven Design approach to simplify the code architecture, dividing it in smaller self-contained contexts. The second is an HTTP Client written in the Vue.js framework to create a responsive and clean user interface. We will show how these changes have minimised maintenance efforts, lessened new features’ development time, reduced technical depths, facilitated newcomers onboarding and also simplified the user experience.

High-performance repetition cat code using fast noisy operations

Bosonic cat qubits stabilized by two-photon driven dissipation benefit from exponential suppression of bit-flip errors and an extensive set of gates preserving this protection. These properties make them promising building blocks of a hardware-efficient and fault-tolerant quantum processor. In this paper, we propose a performance optimization of the repetition cat code architecture using fast but noisy CNOT gates for stabilizer measurements. This optimization leads to high thresholds for the physical figure of merit, given as the ratio between intrinsic single-photon loss rate of the bosonic mode and the engineered two-photon loss rate, as well as an improved scaling below threshold of the required overhead, to reach an expected level of logical error rate. Relying on the specific error models for cat qubit operations, this optimization exploits fast parity measurements, using accelerated low-fidelity CNOT gates, combined with fast ancilla parity-check qubits. The significant enhancement in the performance is explained by: 1- the highly asymmetric error model of cat qubit CNOT gates with a major component on control (ancilla) qubits, and 2- the robustness of the repetition cat code error correction performance in presence of the leakage induced by fast operations. In order to demonstrate these performances, we develop a method to sample the repetition code under circuit-level noise that also takes into account cat qubit state leakage.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework

IntroductionThe usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits. The researchers of this domain have already introduced different kinds of techniques, including data hiding, multiplexing, data compression, color QR Codes, and so on. However, the research on increasing the data storage capacity of the QR Codes is very limited and still operational. ObjectivesThe main objective of this work is to increase the data storage capacity of QR Codes in the IIoT domain. MethodsIn the first part, we have introduced a 4-State-Pattern-based encoding technique to generate the proposed 4-State QR (4SQR) Code where actual data are encoded into a 4SQR Code image which increases the data storage capacity more than the traditional 2-State QR Code. The proposed 4SQR Code consists of four types of patterns, including Black Square Box (BSB), White Square Box (WSB), Triangle, and Circle, whereas the traditional 2-State QR Codes consist of BSB and WSB. In the second part, the 4SQR Code decoding module has been introduced using the adaptive YOLO V5 algorithm where the proposed 4SQR Code image is decoded into the actual data. ResultsThe proposed model is tested in a Digital Twin (DT) framework using randomly generated 3000 testing samples for the encoding module that converts into 4SQR Code images successfully and similarly for the decoding module that decodes the 4SQR Code images into the actual data. ConclusionExperimental results show that this proposed technique offers increased data storage capacity two times than traditional 2-State QR Codes.

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems.

The SCIANTIX code for fission gas behaviour: Status, upgrades, separate-effect validation, and future developments

SCIANTIX is a 0D, open-source code designed to model inert gas behaviour within nuclear fuel at the scale of the grain. The code predominantly employs mechanistic approaches based on kinetic rate-theory models to calculate engineering quantities, such as fission gas release and gaseous fuel swelling. Since its release, SCIANTIX has undergone significant improvements, including the incorporation of new modelling and numerical capabilities. The code architecture has been revamped, embracing an object-orientated structure improving the overall efficiency and usability. This work provides a concise overview of the current state of the SCIANTIX code, highlighting recent updates and advancements. Each SCIANTIX model is presented along with the corresponding separate-effect validation database, which is used to assess its accuracy and predictions.

Development of a ship performance model for power estimation of inland waterway vessels

A ship performance model is an important factor in energy-efficient navigation. It formulates a speed–power relationship that can be used to adjust the engine loads for dynamic energy optimisation. However, currently available models have been developed for sea-going vessels, where the environmental conditions are significantly different from those experienced on inland waterways. Inland waterway shipping has great potential to become a mode of transport that can both improve safety and reduce emissions. Therefore, this paper presents the development of an energy performance model specifically for inland waterway vessels (IWVs). The holistic ship energy system model is based on empirical methods, from resistance to engine performance prediction, established in a modular code architecture. The resistance and propulsion prediction in confined waterways are captured by a newly developed method, considering a superposing of shallow water and bank effect. Verification against model tests and high-fidelity simulations indicate that the selected empirical methods achieved good accuracy for predicting ship performance. The resistance prediction error was 5.2% for single vessels and 8% for pusher-barge convoys based on empirical methods. The results of a case study investigating the performance of a self-propelled vessel under dynamic waterway data, indicate that the developed model could be used for onboard power monitoring and energy optimisation during operation.

Performance Analysis of a Repetition Cat Code Architecture: Computing 256-bit Elliptic Curve Logarithm in 9Hours with 126 133 Cat Qubits.

Cat qubits provide appealing building blocks for quantum computing. They exhibit a tunable noise bias yielding an exponential suppression of bit flips with the average photon number and a protection against the remaining phase errors can be ensured by a simple repetition code. We here quantify the cost of a repetition code and provide valuable guidance for the choice of a large scale architecture using cat qubits by realizing a performance analysis based on the computation of discrete logarithms on an elliptic curve with Shor's algorithm. By focusing on a 2D grid of cat qubits with neighboring connectivity, we propose to implement 2-qubit gates via lattice surgery and Toffoli gates with off-line fault-tolerant preparation of magic states through projective measurements and subsequent gate teleportations. All-to-all connectivity between logical qubits is ensured by routing qubits. Assuming a ratio between single- and two-photon losses of 10^{-5} and a cycle time of 500ns, we show concretely that such an architecture can compute a 256-bit elliptic curve logarithm in 9 h with 126 133 cat qubits and on average 19 photons by cat state. We give the details of the realization of Shor's algorithm so that the proposed performance analysis can be easily reused to guide the choice of architecture for others platforms.

Drift-flux model for upward dispersed two-phase flows in vertical medium-to-large round tubes

The void fraction prediction is critical in nuclear safety analyses. Available one-dimensional computational codes heavily depend on the two-fluid model. The interfacial drag force in the momentum equation of the two-fluid model is currently formulated using the drift-flux model. Thus, the drift-flux model is critical for advanced two-phase flow analysis computational codes. The drift-flux model is characterized by two essential parameters: the distribution parameter and the drift velocity. The distribution parameter expresses the covariance of the area-averaged product of a void fraction and mixture volumetric flux. Flow channel geometry is a significant factor affecting the distribution parameter. The drift velocity expresses the relative velocity between gas and liquid phases. A bubble shape regime affects the drift velocity. The bubble shape regime is susceptible to flow regime dependence on the bubble Reynolds number. Bubbles in a spherical or distorted particle regime (group-1 bubbles) dominate in bubbly flow at a low void fraction, whereas bubbles in a slug or cap bubble regime (group-2 bubbles) play a critical role in beyond-bubbly flow. Whether slug bubbles or cap bubbles appear depends on the channel size. Thus, flow channel size significantly affects the drift velocity. The drift velocity dynamically changes along the two-phase flow, transforming from bubbly to beyond-bubbly flows.The present study first formulates the evolution process of the drift velocity using the two-bubble-group approach. A two-group-based formulation is simplified to model the drift velocity behavior at the bubbly-to-beyond-bubbly flow transition. A scheme is proposed to explicitly calculate the distribution parameter and drift velocity using operating parameters. A drift-flux correlation with the new drift velocity model is evaluated by 750 data collected from 8 different sources. Tested fluid systems are nitrogen-water, air-water, and steam-water systems. Flow channel diameter ranges from 0.0508 to 0.305 m. Test section height (L)-to-channel diameter (D) ratio L/D ranges from 9.41 to 130. Superficial gas velocity is changed from 0.0100 to 11.2 m/s, whereas superficial liquid velocity is changed from 0 (pool condition) to 2.6 m/s. Operating pressure covers from 0.1 to 4.6 MPa. A systematic evaluation demonstrates the validity of the newly developed drift velocity model for a wide range of test conditions. This model is simple enough to be implemented into existing one-dimensional computational codes without any substantial code architecture change.

The Method and Software Tool for Identification of the Machine Code Architecture in Cyberphysical Devices

This work solves the problem of identification of the machine code architecture in cyberphysical devices. A basic systematization of the Executable and Linkable Format and Portable Executable formats of programs, as well as the analysis mechanisms used and the goals achieved, is made. An ontological model of the subject area is constructed, introducing the basic concepts and their relationships. The specificity of the machine code is analyzed, and an analytical record of the process of identifying the architecture of the machine code (MC) processor is obtained. A method for identifying the MC architecture has been synthesized, which includes three successive phases: unpacking the OS image (for a set of identified architectures); building signatures of architectures (their “digital portraits” from the position of MC instructions); identification of the MC architecture for the program under test (using the collected architecture signatures), implemented using four operating modes. A software tool for identifying the MC architecture has been developed in the form of a separate utility that implements the algorithms of the method. The principle of operation of the utility is presented in the form of functional and informational diagrams. Basic testing of the identification utility has been conducted. As a result, a probabilistic assessment of the utility’s work was obtained by assigning various programs to the Top-16 selected architectures.

Infrastructure as Code and Software Architecture Conformance Checking

This article asks the question whether it is possible to take advantage of infrastructure as code to improve the structure and behavior of the software through realizing the deployment concerns of a system's software architecture.

Wireless Deep Video Semantic Transmission

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

OpenVVC Decoder Parameterized and Interfaced Synchronous Dataflow (PiSDF) Model: Tile Based Parallelism.

The emergence of the new video coding standard, Versatile Video Coding (VVC), has resulted in a 40-50% coding gain over its predecessor HEVC for the same visual quality. However, this is accompanied by a sharp increase in computational complexity. The emergence of the VVC standard and the increase in video resolution have exceeded the capacity of single-core architectures. This fact has led researchers to use multicore architectures for the implementation of video standards and to use the parallelism of these architectures for real-time applications. With the strong growth in both areas, video coding and multicore architecture, there is a great need for a design methodology that facilitates the exploration of heterogeneous multicore architectures, which automatically generates optimized code for these architectures in order to reduce time to market. In this context, this paper aims to use the methodology based on data flow modeling associated with the PREESM software. This paper shows how the software has been used to model a complete standard VVC video decoder using Parameterized and Interfaced Synchronous Dataflow (PiSDF) model. The proposed model takes advantage of the parallelism strategies of the OpenVVC decoder and in particular the tile-based parallelism. Experimental results show that the speed of the VVC decoder in PiSDF is slightly higher than the OpenVVC decoder handwritten in C/C++ languages, by up to 11% speedup on a 24-core processor. Thus, the proposed decoder outperforms the state-of-the-art dataflow decoders based on the RVC-CAL model.

An element‐based formulation for ES‐FEM and FS‐FEM models for implementation in standard solid mechanics finite element codes for 2D and 3D static analysis

AbstractEdge‐based and face‐based smoothed finite element methods (ES‐FEM and FS‐FEM, respectively) are modified versions of the finite element method allowing to achieve more accurate results and to reduce sensitivity to mesh distortion, at least for linear elements. These properties make the two methods very attractive. However, their implementation in a standard finite element code is nontrivial because it requires heavy and extensive modifications to the code architecture. In this article, we present an element‐based formulation of ES‐FEM and FS‐FEM methods allowing to implement the two methods in a standard finite element code with no modifications to its architecture. Moreover, the element‐based formulation permits to easily manage any type of element, especially in 3D models where, to the best of the authors' knowledge, only tetrahedral elements are used in FS‐FEM applications found in the literature. Shape functions for non‐simplex 3D elements are proposed in order to apply FS‐FEM to any standard finite element.

All You Need Is Feedback: Communication With Block Attention Feedback Codes

Deep neural network (DNN)-based channel code designs have recently gained interest as an alternative to conventional coding schemes, particularly for channels in which existing codes do not provide satisfactory performance. Coding in the presence of feedback is one such problem, for which promising results have recently been obtained by various DNN-based coding architectures. In this paper, we introduce a novel learning-aided feedback code design, dubbed generalized block attention feedback (GBAF) codes, that achieves order-of-magnitude improvements in block error rate (BLER) compared to existing solutions. Sequence-to-sequence encoding and block-by-block processing of the message bits are the two important design principles of the GBAF codes, which not only reduce the communication overhead, due to fewer interactions between the transmitter and receiver, but also enable flexible coding rates. GBAF codes also have a modular structure that can be implemented using different neural network architectures. In this work, we employ the popular transformer architecture, which outperforms all the prior DNN-based code designs in terms the BLER in the low signal-to-noise ratio regime when the feedback channel is noiseless.