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How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities

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arXiv 2024年

作者： Mohseni, Masoud Scherer, Artur Johnson, K. Grace Wertheim, Oded Otten, Matthew Aadit, Navid Anjum Bresniker, Kirk M. Camsari, Kerem Y. Chapman, Barbara Chatterjee, Soumitra Dagnew, Gebremedhin A. Esposito, Aniello Fahim, Farah Fiorentino, Marco Khalid, Abdullah Kong, Xiangzhou Kulchytskyy, Bohdan Li, Ruoyu Lott, P. Aaron Markov, Igor L. McDermott, Robert F. Pedretti, Giacomo Gajjar, Archit Silva, Allyson Sorebo, John Spentzouris, Panagiotis Steiner, Ziv Torosov, Boyan Venturelli, Davide Visser, Robert J. Webb, Zak Zhan, Xin Cohen, Yonatan Ronagh, Pooya Ho, Alan Beausoleil, Raymond G. Martinis, John M. Hewlett Packard Labs CA United States BC Canada Quantum Machines Israel Department of Physics University of Wisconsin–Madison WI United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Hewlett Packard Enterprise TX United States Fermi National Accelerator Laboratory IL United States Applied Materials CA United States USRA Research Institute for Advanced Computer Science CA United States NASA Ames Research Center CA United States Synopsys CA United States Qolab CA United States Institute for Quantum Computing University of Waterloo ON Canada Department of Physics & Astronomy University of Waterloo ON Canada Perimeter Institute for Theoretical Physics ON Canada

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction procedures on a single logical qubit. Nevertheless, despite significant progress and excitement, the detailed path toward a full-stack scalable quantum computing technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation and learning/modeling quantum data generated by natural or engineered quantum systems. In order to estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We show orders of magnitude enhancement in performance could be obtained by a combination of quantum hardware and algorithmic improvements. Furthermore, we argue that, to tackle today’s industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted p

关键词： Qubits

Logical quantum processor based on reconfigurable atom arrays

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arXiv 2023年

作者： Bluvstein, Dolev Evered, Simon J. Geim, Alexandra A. Li, Sophie H. Zhou, Hengyun Manovitz, Tom Ebadi, Sepehr Cain, Madelyn Kalinowski, Marcin Hangleiter, Dominik Ataides, J. Pablo Bonilla Maskara, Nishad Cong, Iris Gao, Xun Rodriguez, Pedro Sales Karolyshyn, Thomas Semeghini, Giulia Gullans, Michael J. Greiner, Markus Vuletić, Vladan Lukin, Mikhail D. Department of Physics Harvard University CambridgeMA02138 United States QuEra Computing Inc. BostonMA02135 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States

Suppressing errors is the central challenge for useful quantum computing [1], requiring quantum error correction [2-6] for large-scale processing. However, the overhead in the realization of error-corrected "logical" qubits, where information is encoded across many physical qubits for redundancy [2-4], poses significant challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Utilizing logical-level control and a zoned architecture in reconfigurable neutral atom arrays [7], our system combines high two-qubit gate fidelities [8], arbitrary connectivity [7, 9], as well as fully programmable single-qubit rotations and mid-circuit readout [10-15]. Operating this logical processor with various types of encodings, we demonstrate improvement of a two-qubit logic gate by scaling surface code [6] distance from d = 3 to d = 7, preparation of color code qubits with break-even fidelities [5], fault-tolerant creation of logical GHZ states and feedforward entanglement teleportation, as well as operation of 40 color code qubits. Finally, using three-dimensional [[8,3,2]] code blocks [16, 17], we realize computationally complex sampling circuits [18] with up to 48 logical qubits entangled with hypercube connectivity [19] with 228 logical two-qubit gates and 48 logical CCZ gates [20]. We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling [21, 22]. These results herald the advent of early error-corrected quantum computation and chart a path toward large-scale logical processors. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

Optimal management adaptive of two crossroads by genetic algorithm

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International Journal of Modeling, Simulation, and Scientific computing 2019年第3期10卷 1-14页

作者： Amal Merbah Abdelilah Makrizi Department of Applied Mathematics and Computer Science Faculty of Sciences and Technics Hassan First University Km 3Route of CasablancaSettatPOB:577/Morocco Department of Applied Mathematics and Computer Science Faculty of Sciences and Technics Hassan First University Laboratory of MathematicsComputing and Engineering Sciences Km 3Route of CasablancaSettatPOB:577/Morocco

This paper addresses the issue of managing the traffic flow at traffic-light controlled junctions adopting a mixed,nonlinear programming *** precisely,we adopt an adaptive,acyclic control system based on the genetic algorithm(GA),a system that allows to accurately decide in real time on the light switching pattern that minimizes the traffic waiting *** importantly,the inherent crossroads constraints,namely,the security of their users and the structure thereof,also referred to as the antagonism principle,are considered in the proceeding of this *** implementation of this model on two crossroads significantly decreases the waiting time and regulates the traffic ***,the adaptive control system based on the GA proves efficient compared to the nonadaptive system.

关键词： Traffic control an adaptive regulation system traffic lights colors network of crossroads

Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications

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Scientific reports 2025年第1期15卷 18720页

作者： Amal K Alkhalifa Mohammed Aljebreen Rakan Alanazi Nazir Ahmad Sultan Alahmari Othman Alrusaini Ali Alqazzaz Hassan Alkhiri Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Kingdom of Saudi Arabia. Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh 11437 Kingdom of Saudi Arabia. Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Kingdom of Saudi Arabia. rakan.nalenezi@nbu.edu.sa. Department of Computer Science Applied College at Mahayil King Khalid University Abha Kingdom of Saudi Arabia. King Abdul Aziz City for Science and Technology (KACST) Cybersecurity Institute Riyadh Kingdom of Saudi Arabia. Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia. Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia. Department of Computer Science Faculty of Computing and Information Technology Al-Baha University Al Bahah Kingdom of Saudi Arabia.

Denial of Wallet (DoW) attacks are one kind of cyberattack whose goal is to develop and expand the financial sources of a group by causing extreme costs in their serverless computing or cloud environments. These threats are chiefly related to serverless structures owing to their features, such as auto-scaling, pay-as-you-go method, cost amplification, and limited control. Serverless computing, Function-as-a-Service (FaaS), is a cloud computing (CC) system that permits developers to construct and run applications without a conventional server substructure. The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. This study proposes a Mitigating Malicious Denial of Wallet Attack using Attribute Reduction with Deep Learning (MMDoWA-ARDL) approach for serverless computing on next-generation applications. The primary purpose of the MMDoWA-ARDL approach is to propose a novel framework that effectively detects and mitigates malicious attacks in serverless environments using an advanced deep-learning model. Initially, the presented MMDoWA-ARDL model applies data pre-processing using Z-score normalization to transform input data into a valid format. Furthermore, the feature selection process-based cuckoo search optimization (CSO) model efficiently identifies the most impactful attributes related to potential malicious activity. For the DoW attack mitigation process, the bi-directional long short-term memory multi-head self-attention network (BMNet) method is employed. Finally, the hyperparameter tuning is accomplished by implementing the secretary bird optimizer algorithm (SBOA) method to enhance the classification outcomes of the BMNet model. A wide-ranging experimental investigation uses a benchmark dataset to exhibit the superior performance of the proposed MMDoWA-ARDL technique. The compari

关键词： Cloud computing Cybersecurity Deep learning Denial of wallet Secretary bird optimization Serverless computing

Multiple Kernel Representation Learning on Networks

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arXiv 2021年

作者： Çelikkanat, Abdulkadir Shen, Yanning Malliaros, Fragkiskos D. The Department of Applied Mathematics and Computer Science Technical University of Denmark 2800 Kgs. Lyngby Denmark Gif-sur-Yvette91190 France The Department of EECS The Center for Pervasive Communications and Computing University of California IrvineCA92697 United States

Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classification, and visualization. Two popular approaches for this problem are matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning node representations. In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network. The benefit of this novel formulation is that it enables us to utilize kernel functions without realizing the exact proximity matrix so that it enhances the expressiveness of existing matrix decomposition methods with kernels and alleviates their computational complexities. We extend the approach with a multiple kernel learning formulation that provides the flexibility of learning the kernel as the linear combination of a dictionary of kernels in data-driven fashion. We perform an empirical evaluation on real-world networks, showing that the proposed model outperforms baseline node embedding algorithms in downstream machine learning tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

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arXiv 2023年

作者： Balewski, Jan Amankwah, Mercy G. Van Beeumen, Roel Bethel, E. Wes Perciano, Talita Camps, Daan National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Mathematics Case Western Reserve University ClevelandOH United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Department San Francisco State University San FranciscoCA United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

Studying the Influence of Electroporation on HT29 Cell Line Interaction with Fibronectin and Collagen Protein Micro-Patterned Surface 2

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Studying the Influence of Electroporation on HT29 Cell Line ...

2nd Joint International Conference on Emerging computing Technology and Sports, JICETS 2019

作者： Mamman, Hassan Buhari Jamil, Muhammad Mahadi Abdul Ibrahim, Tengku Nadzlin Tengku Wahab, Mohd Helmy Abd Sharif, Johan Mohamad Rahman, Nur Adilah Abd Youseffi, Mansour Javid, Farideh Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Department of Computer Science School of Computing Universiti Teknologi Malaysia School of Applied Sciences University of Huddersfield United Kingdom Biomedical and Electronics Engineering Department Faculty of Engineering and Informatics University of Bradford United Kingdom

Micro-contact printing (MCP) is a scheme that allows a substrate or surface to be functionalized freely with extracellular matrix (ECM) protein such as fibronectin and collagen, in a well-defined manner. MCP can be used to regulate cell adhesion geometry on a substrate and in controlling wound healing process by facilitating directed cell migration. In this study, human colon cancer cell line, HT29 were grown on a micro-contact printed pattern of fibronectin and collagen protein with repeat gratings of 25μm, 50μm, and 100μm wide, for 48 hours. The cells alignments to the patterned substrates were then computed, where 0° means 100% alignment to the pattern. This was done with the purpose of finding those pattern that stimulated the best degree of cell alignment. Best alignment and elongation were obtained on 50μm of the two ECM proteins. The quantitative analysis of the results revealed that HT29 cells aligned most readily to the 50μm width pattern with a mean angle of alignment of 5.0-1.3 and 16.1-4.6, respectively, on fibronectin and collagen pattern surfaces. Contrarily, the cells aligned poorly on the 25μm width pattern of fibronectin, collagen and the control substrates with a mean angle of 33.4-8.4, 36.2-8.9 and 54.5-6.0, respectively. Furthermore, the 50μm stamp pattern was used to investigate the influence of pulse electric field (PEF) on the HT29 alignment to the patterned substrate. The result revealed that there was significant improvement (P © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Collagen

Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

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arXiv 2024年

作者： Hu, Yaowen Song, Yunxiang Zhu, Xinrui Guo, Xiangwen Lu, Shengyuan Zhang, Qihang He, Lingyan Franken, C.A.A. Powell, Keith Warner, Hana Assumpcao, Daniel Renaud, Dylan Wang, Ying Magalhães, Letícia Rosborough, Victoria Shams-Ansari, Amirhassan Li, Xudong Cheng, Rebecca Luke, Kevin Yang, Kiyoul Barbastathis, George Zhang, Mian Zhu, Di Johansson, Leif Beling, Andreas Sinclair, Neil Loncar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Beijing100871 China Quantum Science and Engineering Harvard University CambridgeMA02138 United States Department of Electrical and Computing Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States HyperLight Corporation 501 Massachusetts Ave CambridgeMA02139 United States Laser Physics and Nonlinear Optics Group Department of Science and Technology MESA+ Institute of Nanotechnology University of Twente Enschede Netherlands Freedom Photonics 41 Aero Camino GoletaCA United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Singapore138634 Singapore

The surge in artificial intelligence applications, encompassing machine vision, autonomous driving, remote sensing, and process simulation, relies on efficient and large-scale data processing. This requires highly scalable computation with high speed and low energy consumption, calling for innovative solutions to overcome the limitations of electronic systems1–9. Integrated photonics may play a pivotal role in addressing this challenge, leveraging the intrinsic properties of photons, such as natural parallelism, high bandwidth, and low latency10–15. While current photonic computing approaches show promise, they are critically limited by the speed and energy consumption of mapping information from the electronic to optical domain (i.e., electro-optic conversion)1–3,6–8,16 . Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-film lithium niobate high-performance components working synergistically, surpassing state-of-the-art systems on this platform. We further demonstrate binary-classification, handwritten-digit classification, and image classification with remarkable accuracy, showcasing our system’s capability of executing real algorithms. Finally, we investigate the opportunities offered by combining our system with a hybrid-integrated distributed feedback laser source and a heterogeneous-integrated modified uni-traveling carrier photodiode. Our results illustrate the promise of thin-film lithium niobate as a computational platform, addressing current bottlenecks in both electronic and photonic computation. Its unique properties of high-performance electro-optic weight encoding and conversion, wafer-scale scalability, and compatibility with integrated lasers and

关键词： Distributed feedback lasers

Gender bias in transformers: A comprehensive review of detection and mitigation strategies

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Natural Language Processing Journal

Natural Language Processing Journal 2024年 6卷

作者： Praneeth Nemani Yericherla Deepak Joel Palla Vijay Farhana Ferdouzi Liza Department of Computer Science and Engineering IIIT Naya Raipur Chhattisgarh India College of Engineering and Applied Science University of Colorado Boulder CO USA Department of Data Science and Artificial Intelligence IIIT Naya Raipur Chhattisgarh India School of Computing Sciences University of East Anglia (UEA) Norwich United Kingdom

Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals’ lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective. While the existence of gender bias in language models has been acknowledged in previous studies, there remains a lack of consensus on how to measure and evaluate this bias effectively. Our survey critically examines the existing literature on gender bias in Transformers, shedding light on the diverse methodologies and metrics employed to assess bias. Several limitations in current approaches to measuring gender bias in Transformers are identified, encompassing the utilization of incomplete or flawed metrics, inadequate dataset sizes, and a dearth of standardization in evaluation methods. Furthermore, our survey delves into the potential ramifications of gender bias in Transformers for downstream applications, including dialogue systems and machine translation. We underscore the importance of fostering equity and fairness in these systems by emphasizing the need for heightened awareness and accountability in developing and deploying language technologies. This paper serves as a comprehensive overview of gender bias in Transformer models, providing novel insights and offering valuable directions for future research in this critical domain.

关键词： Gender bias Transformer models Bias mitigation Binary gender assumption Self-attention