Computer Hardware Research Articles

Performance Improvement of Degrading Memristor-Bridge-Based Multilayer Neural Network with Refresh Pulses

Memristors as non-volatile memory devices have been recognized for executing in-memory computation in neuromorphic hardware. In this paper, a multilayer neural network has been developed with memristor-bridges as electrical synapses and trained with modified-chip-in-the-loop technique for an image classification task. Modeling the ideal conduction behavior of memristors by their device-physics inspired analytical model has yielded satisfactory performance. However, repeated voltage cycling degrades the resistance window of memristors by aggregating conductive residuals in filamentary memristors. Therefore, emulation of such nonideality has demonstrated compromised results. To improve the performance, refresh pulses have been introduced to the devices in between write pulses to eradicate the fundamental reason of the degradation — i.e., the residuals. It has been observed that improvement of performance is contingent upon the refreshment frequency, and frequent refreshment has the ability to restore performance to a level closely approaching its ideal emulation.

Pengenalan Perangkat Komputer Dasar sebagai Upaya Meningkatkan Pengetahuan Siswa SDI Waioti Maumere

In today's digital era, basic computer and Microsoft literacy training is very important, both for elementary school students and professional workers. Increasing use is recognized by several sectors, including the banking industry and the education sector. To overcome this, basic computer introduction activities were carried out in the context of outreach to the community, especially elementary school students. From the information found at the SDI Waioti Maumere Elementary School, some students are not yet familiar with current computer technology. So the service carried out aims to provide an introduction to basic computers to elementary school students at SDI Waioti Maumere, including an introduction to computer hardware and an understanding of using Microsoft Word as an effort to improve each student's ability to adapt to today's global world. The three steps in this activity process are preparation, implementation, and assessment. In this activity, the committee succeeded in initiating basic computer and Microsoft Word learning for 36 students divided into two classes, namely A and B.

The ICT Competencies Required for the General Education Teachers from the Computer Teachers’ Viewpoints

This study aims to explore the viewpoints of computer teachers about the ICT competencies required for general education teachers in the State of Qatar. A questionnaire was designed, validated, and administered to a sample consisted of 504 male and female computer teachers. The results revealed the following: There are differences in the levels of importance in the three areas of the general education teachers’ ICT competencies according to the computer teachers’ viewpoints, ordered as follows: Operating System & Software, Internet & E-learning, and Computer Hardware & Accessories. No significant differences were found between the variables of the study: (gender, years of experience, academic qualifications, and education levels of computer teachers). And an interaction between the variables in the area of Operating System & Software was found. A significant difference was found between the male & female in favor of female computer teachers in the area of computer hardware & accessories. No significant difference was found between the other variables of the study in this area, and no significant differences were found between the interactions of the variables of the study in this area. A significant difference was found between years of experience in favor of the computer teachers who have more than 20 years of teaching experience in the area of Internet & E-learning, and no significant difference was found between the other variables of the study in this area. A significant difference was found between the interactions of the variables of qualifications & the years of experience in favor of the years of experience in this area, and no significant differences were found between the interactions of other variables of this study in this area. The results were interpreted and some recommendations and further studies were suggested.

CO2 emission mitigation of a hybrid photovoltaic and cogeneration system in computer hardware manufacturing industry: A case study in Thailand

Abstract In the wake of COP26 and the growing urgency of addressing climate change, achieving carbon neutrality by 2050 has become a central global objective. This imperative extends to industries like computer hardware manufacturing, which are now actively pursuing decarbonization strategies through the strategic adoption of renewable energy sources and energy efficiency enhancements. This research paper assessed the CO2 emission mitigation potential of a hybrid system of photovoltaic (PV) roof and cogeneration where a large factory of computer hardware manufacturing in tropical Thailand was selected as a study site. On one hand, a one-Megawatt photovoltaic system was installed over the roof of the production building to generate electricity from solar radiation to serve the building. On the other hand, a twenty-four-Megawatt cogeneration system of gas engines as the prime mover was used to supply power to meet the building’s electricity demand. Waste heat from the gas engine was used by the absorption chiller to generate chilled water for cooling inside the building. Based on the system equipment specifications, the annual simulation using Thailand’s solar radiation showed that the installed photovoltaic system could generate electricity of 1,412.4 MWhelec/year while the implementation of the absorption chillers for cooling helped to reduce the electrical energy consumed by the traditional electric chiller by 10,211.4 MWhelec/year. In our study case where the CO2 emission of the grid power was 0.4758 kgCO2/kWhelec in the year 2022 and was reduced to 0.350 kgCO2/kWhelec in the year 2050, the total CO2 emission mitigation from the hybrid photovoltaic and cogeneration system with the genset efficiency of 50% and the waste heat recovery of 60% could reduce approximately 207,388.5TonCO2 for over 20 years as compared to the scenario where the grid electricity alone powered the building. These findings underscored the critical role of the proposed hybrid system in addressing the climate crisis and exemplified how the industry could make meaningful strides toward more environmental sustainability.

A TEMPEST Attack Implementation based on Hidden Markov model in Smart Grid

Abstract A Smart Grid contains a large number of terminals to gather voltage, current, and power data. If these terminals are attacked by hackers to steal data and disrupt the operation of the grid, the grid will encounter a huge challenge. And side channel attack is an attack way that uses information signals (such as electromagnetic radiation, and computer hardware running sound) inadvertently released by the computer to decipher. The computer keyboard generates electromagnetic radiation signals during operation, and hackers may acquire your account and password through the keyboard’s tapping sound, resulting in the leakage of important data. This article analyzes the electromagnetic radiation leakage vulnerabilities of keyboards and designs a way to implement a TEMPEST (Transient Electromagnetic Pulse Emanation Surveillance Technology) Attack based on the Hidden Markov model, which can provide a foundation for avoiding potential risk.

Assessment of energy saving and CO2 emission mitigation of low-lift cooling in computer hardware manufacturing industry: A case study in Thailand

Abstract Released heat from production process contributes the largest proportion of cooling load in computer hardware manufacturing industry. According to traditional design practice, chiller plant produces chilled water at a temperature of 7°C to treat the entire load from the production process and from the comfort air-conditioning in buildings. However, the heat from the process is solely the sensible component where high-temperature chilled water (e.g., 15°C) can be used for the heat removal, the chillers equipped with low temperature-lift technology thus offer a great opportunity to improve the plant’s cooling performance. Under the critical global warming situation and the need of decarbonizing industry, this paper investigated the energy saving and CO2 emission mitigation potential of the low-lift technology by which the chiller plant of high-temperature chilled water (15°C) is dedicated for the production process cooling, and the traditional chiller plant of the low-temperature chilled water is used to serve the building air-conditioning. The study site was a large factory of computer hardware manufacturing in Thailand. Through detailed energy auditing, the cooling load profile of the production process was established, and it was used to design the low-lift chiller plant. The annual simulation results showed that in our study case the low-lift chiller plant together with variable motor speed control had 31.6% higher cooling performance than the traditional chiller plant. The plant could reduce the energy consumption by 5,550 MWhelec/year. As the CO2 emission of the grid power in Thailand was 0.47 kgCO2/kWh in 2023 and it was expected to reduce to 0.35 kgCO2/kWh in 2050, the CO2 emissions from implementing the low-lift cooling could be reduced by 49,400 TonCO2 over a course of the 20-year project lifetime. The study results encouraged the low-lift cooling technology as a practical energy efficiency measure in computer hardware manufacturing industry. To our knowledge, while past research focused on the low-lift cooling in building air-conditioning, the study was the first potential assessment project of the low-lift cooling as a deep decarbonization technology for the computer hardware manufacturing industry in Thailand.

Optimizing the utilisation of different computational hardware types for real time simulation

Optimizing the utilisation of different computational hardware types for real time simulation

RockDNet: Deep Learning Approach for Lithology Classification

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

State Machines for Large Scale Computer Software and Systems

The behavior and architecture of large scale discrete state systems found in computer software and hardware can be specified and analyzed using a particular class of primitive recursive functions. This article begins with an illustration of the utility of the method via a number of small examples and then via longer specification and verification of the “Paxos” distributed consensus algorithm [ 26 ]. The “sequence maps” are then shown to provide an alternative representation of deterministic state machines and products of state machines. Distributed and composite systems, parallel and concurrent computation, and real-time behavior can all be specified naturally with these methods—which require neither extensions to the classical state machine model nor any axiomatic methods or other techniques from formal logic or other foundational methods. Compared to state diagrams or tables or the standard set-tuple-transition-maps, sequence maps are more concise and better suited to describing the behavior and compositional architecture of computer systems. Staying strictly within the boundaries of classical deterministic state machines anchors the methods to the algebraic structures of automata and makes the specifications faithful to engineering practice.

Random Projection‐Based Locality‐Sensitive Hashing in a Memristor Crossbar Array with Stochasticity for Sparse Self‐Attention‐Based Transformer

AbstractSelf‐attention mechanism is critically central to the state‐of‐the‐art transformer models. Because the standard full self‐attention has quadratic complexity with respect to the input's length L, resulting in prohibitively large memory for very long sequences, sparse self‐attention enabled by random projection (RP)‐based locality‐sensitive hashing (LSH) has recently been proposed to reduce the complexity to O(L log L). However, in current digital computing hardware with a von Neumann architecture, RP, which is essentially a matrix multiplication operation, incurs unavoidable time and energy‐consuming data shuttling between off‐chip memory and processing units. In addition, it is known that digital computers simply cannot generate provably random numbers. With the emerging analog memristive technology, it is shown that it is feasible to harness the intrinsic device‐to‐device variability in the memristor crossbar array for implementing the RP matrix and perform RP‐LSH computation in memory. On this basis, sequence prediction tasks are performed with a sparse self‐attention‐based Transformer in a hybrid software‐hardware approach, achieving a testing accuracy over 70% with much less computational complexity. By further harnessing the cycle‐to‐cycle variability for multi‐round hashing, 12% increase in the testing accuracy is demonstrated. This work extends the range of applications of memristor crossbar arrays to the state‐of‐the‐art large language models (LLMs).

ScaleLat: A chemical structure matching algorithm for mapping atomic structure of multi-phase system and high entropy alloys

ScaleLat (Scale Lattice) is a computer program written in C for performing the atomic structure analysis of multi-phase system or high entropy alloys (HEAs). The program implements an atomic cluster cell extraction algorithm to obtain all symmetry independent characteristic atomic cluster cells for the complex atomic configurations which are usually obtained from molecular dynamics or kinetic Monte-Carlo simulations at nanoscale or mesoscopic scale. ScaleLat implements an efficient and unique chemical structure matching algorithm to match all extracted atomic clusters from a large supercell (>104 atoms) to a representative small one (∼ 103 or less), providing the possibility to directly use the highly accurate quantum mechanical methods to study the electronic, magnetic, and mechanical properties of multi-component alloys for complex microstructures. We demonstrate the capability of ScaleLat code by conducting both the atomic structure matching analysis for Fe-12.8 at.% Cr binary alloy and equiatomic CrFeCoNiCu high entropy alloy, successfully obtaining the representative supercells containing 102∼103 atoms for two systems. The reliability of the proposed chemical structure matching scheme is tested and confirmed by calculating the electronic structures of both examples using trial supercells with various sizes. Overall, ScaleLat program provides a universal platform to efficiently map all essential chemical structures of large complex atomic structures to a relatively easy-handling small supercell for quantum mechanical calculations of various user interested properties. Program SummaryProgram Title: ScaleLatCPC Library link to program files: https://doi.org/10.17632/cv6wsxy938.1Developer's repository link: https://github.com/NanLi-xjtu/ScaleLat.gitLicensing provisions: MITProgramming language: CNature of Problem: Very large supercells containing more than 104 atoms must be used to describe the atomic structure of multi-phase materials or high entropy alloys, and their electronic and mechanical properties are almost not possible to be investigated using quantum mechanical calculations within conventional computational hardware. Decreasing the total number of atoms in a smaller representative supercell which preserves all essential chemical structures of the original large supercell is the key to tackling the problem.Solution to the Problem: An atomic cluster cell extraction algorithm is realized to thoroughly obtain all symmetry independent characteristic chemical structures of multi-phase system or high entropy alloys. Utilizing the direct atom swapping method, the efficient chemical structure matching procedure is developed to map all extracted characteristic atomic cluster cells of benchmark structure to the small supercell by minimizing their differences in both the types and relative fractions of atomic cluster cell sets.

Pengembangan Video Pembelajaran Berbasis Kearifan Lokal Pada Pelajaran TIK di Kelas VII Sekolah MTs Ar-Rahman Wampu

This research aims to develop and assess the effectiveness of locally wisdom-based instructional videos on computer hardware topics for seventh-grade students at MTs Ar Rahman Wampu, Langkat Regency, North Sumatra. The research method employed is developmental research, utilizing the 4-D model proposed by Thiagarajan, encompassing the stages of defining, designing, developing, and disseminating. The study is conducted at MTs Ar Rahman Wampu over a two-month period, with seventh-grade students randomly selected as research subjects. The object of the study is the instructional video based on local wisdom concerning computer hardware. Research instruments include a video validation sheet, student activity observation sheet, and questionnaires measuring student responses and learning interests. Data analysis involves evaluating the validity, practicality, and effectiveness of the instructional video, utilizing percentages and predefined criteria. The subsequent practicality analysis incorporates expert assessments and field observations. The effectiveness of the learning process is assessed through student tests, percentage of activity time, and positive responses to the instructional video. The enhancement of learning interest is measured through normalized gain calculations. The expected outcome of this research is to contribute to the development of innovative and high-quality instructional videos within the context of local wisdom, especially in the field of ICT education at the MTs level. Keywords: Instructional Video, Local Wisdom, Instructional Development

Optimization of intelligent guided vehicle vision navigation based on improved YOLOv2.

Addressing the challenge of limited accuracy and real-time performance in intelligent guided vehicle (IGV) image recognition and detection, typically reliant on traditional feature extraction approaches. This study delves into a visual navigation detection method using an improved You Only Look Once (YOLO) model-simplified YOLOv2 (SYOLOv2) to satisfy the complex operating conditions of the port and the limitations of IGV hardware computing. The convolutional neural network structure of YOLOv2 is refined to ensure adaptability to varying weather conditions using a single image. Preprocessing of images involves Contrast Limited Adaptive Histogram Equalization (CLAHE), while an adaptive image resolution detection model, contingent upon vehicle speed, is proposed to enhance the detection performance. The comparative experiments conducted on image datasets reflective of actual road conditions and weather conditions demonstrate notable enhancements in accuracy and frames transmitted per second compared to conventional methods. These improvements signify the efficacy of the proposed approach in meeting the stringent requirements for real-time detection on IGV platforms.

Additional Acceleration of Antenna Optimal Characterizationwith Modeling Support

Optimal characterization is assumed to provide the best solution for the designed cost function among the possible solutions within the specified range. These processes can take a long time depending on the applications and computer hardware used. Here, the optimization process is supported by ANN modeling in order to shorten the current optimization processes as much as possible. For this purpose, the selection of design parameters of the bowtie patch antenna (BPA) is presented as a multi-dimensional, multi-objective modeling-supported design optimization problem. The operating frequency of the proposed antenna is 28 GHz, which is the standard for millimeter wave band and 5G technologies. To overcome this challenging design optimization, a new, fast and powerful optimization algorithm was used by modifying the non-dominant sorting genetic algorithm (NSGA)-III, and the optimal characterization of the microwave antenna design was achieved. Although the proposed method gives the same results compared to the existing process, it takes much less time. Therefore, it is possible to shorten the process and reduce costs without the need for extra applications or hardware. As a whole, the proposed design optimization process is an efficient, fast and reliable solution for all design problems.

Integration effect of artificial intelligence and traditional animation creation technology

Abstract Despite the advancements in modern computer hardware and software, the creation of digital animation still demands a substantial investment of both manpower and time. This article aimed to explore how artificial intelligence (AI) technology can be combined with traditional animation creation techniques to achieve better integration effects. By combining intelligent character animation generation with hand drawing, a generative adversarial network was used to achieve high-quality animation generation. The generator generated realistic animations, and the discriminator measured the authenticity of the animations by comparing the differences between the generator-generated animations and the real animations, which was used for automated character animation generation. This can greatly reduce the cost and time of digital animation creation, improve the quality of digital animation, and provide more innovation for the application of traditional animation technology. The average number of audience attracted by characters through blended creation was 122.2% more than that of characters through traditional animation creation and 32.6% more than that of characters through AI creation. This not only helps animation producers complete animation production more quickly, but also enhances the creativity and artistic value of animation.

АРХІТЕКТУРА СИСТЕМИ АВТОНОМНОГО КЕРУВАННЯ ДЛЯ FPV-ДРОНІВ

Drone technologies and robotic systems, that can be used to optimize or replace human labor in different areas, are continuing to develop and expand their area of application. Computational hardware, that can be used as an extension to basic drone hardware, is becoming more powerful for a smaller price and size. Autonomous drone control provides a practical solution for issues that are present when a drone is controlled manually by an operator. There are limitations that exclude the possibility of operator control and some solutions are much less effective if there is no autonomous algorithm. A large distance, radio interference or a huge amount of data to be analyzed are not as critical if a drone is capable to make decisions autonomously without an operator. Because of the features of a basic amateur FPV-drone its flight controller does not operate enough computing power to execute complex algorithms such as object detection, object tracking, calculation of a proper trajectory etc. In cases like this a companion computer should be used. Depending on an area of application, drone control algorithms may vary. Flight controllers’ firmware has some embedded functionality with relatively simple algorithms like pre-defined flight routes that use GCS-coordinates. In the case when more complex behavior is required (image processing, AI integration or autonomous decision making with manual controls), such firmware provides an ability to accept various drone commands that correspond to the MAVLink communication protocol. This work investigates important details of companion computers integration with flight controllers with examples and describes hardware and software aspects of implementation of autonomous drone controls. The research also provides solutions to problems that can be possible when implementing such a system.

A journey in unraveling the enantiorecognition mechanism of 3,5-dinitrobenzoyl-amino acids with two Cinchona alkaloid-based chiral stationary phases: The power of molecular dynamic simulations

BackgroundInnovations in computer hardware and software capabilities have paved the way for advances in molecular modelling techniques and methods, leading to an unprecedented expansion of their potential applications. In contrast to the docking technique, which usually identifies the most stable selector-selectand (SO-SA) complex for each enantiomer, the molecular dynamics (MD) technique enables the consideration of a distribution of the SO-SA complexes based on their energy profile. This approach provides a more truthful representation of the processes occurring within the column. However, benchmark procedures and focused guidelines for computational treatment of enantioselectivity at the molecular level are still missing. ResultsTwenty-eight molecular dynamics simulations were performed to study the enantiorecognition mechanisms of seven N-3,5-dinitrobenzoylated α- and β-amino acids (DNB-AAs), occurring with the two quinine- and quinidine-based (QN-AX and QD-AX) chiral stationary phases (CSPs), under polar-ionic conditions. The MD protocol was optimized in terms of box size, simulation run time, and frame recording frequency. Subsequently, all the trajectories were analyzed by calculating both the type and amount of the interactions engaged by the selectands (SAs) with the two chiral selectors (SOs), as well as the conformational and interaction energy profiles of the formed SA-SO associates. All the MDs were in strict agreement with the experimental enantiomeric elution order and allowed to establish (i) that salt-bridge and H-bond interactions play a pivotal role in the enantiorecognition mechanisms, and (ii) that the π-cation and π-π interactions are the discriminant chemical features between the two SOs in ruling the chiral recognition mechanism. SignificanceThe results of this work clearly demonstrate the high contribution given by MD simulations in the comprehension of the enantiorecognition mechanism with Cinchona alkaloid-based CSPs. However, from this research endeavor it clearly emerged that the MD protocol optimization is crucial for the quality of the produced results.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings. Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware. Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training. Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Full quantum tomography study of Google’s Sycamore gate on IBM’s quantum computers

The potential of achieving computational hardware with quantum advantage depends heavily on the quality of quantum gate operations. However, the presence of imperfect two-qubit gates poses a significant challenge and acts as a major obstacle in developing scalable quantum information processors. Google’s Quantum AI and collaborators claimed to have conducted a supremacy regime experiment. In this experiment, a new two-qubit universal gate called the Sycamore gate is constructed and employed to generate random quantum circuits (RQCs), using a programmable quantum processor with 53 qubits. These computations were carried out in a computational state space of size 9×1015\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$9 \ imes 10^{15}$\\end{document}. Nevertheless, even in strictly-controlled laboratory settings, quantum information on quantum processors is susceptible to various disturbances, including undesired interaction with the surroundings and imperfections in the quantum state. To address this issue, we conduct both quantum state tomography (QST) and quantum process tomography (QPT) experiments on Google’s Sycamore gate using different artificial architectural superconducting quantum computer. Furthermore, to demonstrate how errors affect gate fidelity at the level of quantum circuits, we design and conduct full QST experiments for the five-qubit eight-cycle circuit, which was introduced as an example of the programability of Google’s Sycamore quantum processor. These quantum tomography experiments are conducted in three distinct environments: noise-free, noisy simulation, and on IBM Quantum’s genuine quantum computer. Our results offer valuable insights into the performance of IBM Quantum’s hardware and the robustness of Sycamore gates within this experimental setup. These findings contribute to our understanding of quantum hardware performance and provide valuable information for optimizing quantum algorithms for practical applications.