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Letteratura scientifica selezionata sul tema "Hardware for Artificial Intelligence"

Autore: Grafiati

Pubblicato: 15 giugno 2024

Ultima modifica: 19 luglio 2025

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Articoli di riviste sul tema "Hardware for Artificial Intelligence"

Burkert, Andreas. "Hardware for Artificial Intelligence." ATZ worldwide 121, no. 5 (2019): 8–13. http://dx.doi.org/10.1007/s38311-019-0060-0.

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Burkert, Andreas. "Hardware for Artificial Intelligence." ATZelectronics worldwide 14, no. 3 (2019): 8–13. http://dx.doi.org/10.1007/s38314-019-0026-4.

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Popov, I. "SoC hardware supporting artificial intelligence." ELECTRONICS: Science, Technology, Business, no. 7 (2018): 116–23. http://dx.doi.org/10.22184/1992-4178.2018.178.7.116.123.

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Albin, Vincent Vettukattil. "Artificial Intelligence." Vidyankur Journal of Philosophical & Theological Studies XXV, February 2023, no. 1 (2023): 50–58. https://doi.org/10.5281/zenodo.7642777.

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In the contemporary scenario, technology has become a part and parcel of our life. In fact, the aim of technology is to help humans transcend their limitations. Artificial intelligence (AI) and related hardware is one such technology that is bringing about revolutionary changes in the various domains of human life.

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VerWey, John. "The Other Artificial Intelligence Hardware Problem." Computer 55, no. 1 (2022): 34–42. http://dx.doi.org/10.1109/mc.2021.3113271.

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Johnson, Jeffrey, Peter Denning, Andrew Odlyzko, et al. "Artificial Intelligence: Foundational Technologies of Artificial Intelligence." Ubiquity 2025, July (2025): 1–10. https://doi.org/10.1145/3747355.

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More than the 70 years since its emergence in the early 1950s, artificial intelligence (AI) is performing cognitive tasks traditionally considered the unique province of humans. This progress did not occur in a vacuum. AI emerged against a rich background of technologies from computer science and ideas about intelligence and learning from philosophy, psychology, logic, game theory, and cognitive science. We sketch out the enabling technologies for AI. They include search, reasoning, neural networks, natural language processing, signal processing and computer graphics, programming and conventional software engineering, human-computer interaction, communications, and specialized hardware that provides supercomputing power. Beyond these technologies is the notion of Artificial General Intelligence that has or exceeds the capabilities of the human brain. Currently this is completely aspirational and is not expected to be possible before 2025, if ever. Artificial Intelligence is based on a variety of technologies, none of which seek to emulate human intelligence.

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Raj, Anish. "Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 646–55. http://dx.doi.org/10.22214/ijraset.2024.64695.

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Artificial intelligence is a science that aims to perform tasks that require human intelligence. For the past two years, it has been used as a development tool in many areas such as prediction, health, security, and also improves the performance of production and services. Since artificial intelligence and its operations are based on too much data, algorithms, and scientific data, users cannot understand and comprehend the content and do not have the skills necessary to use this technology. Since artificial intelligence is controlled by machines and algorithms, it is difficult to determine the cause of system software and hardware crashes. Using this system requires significant funding. However, there are some facts that support the adoption of AI, such as the availability of transferable power in the cloud, the availability of simple software libraries, and the availability of data. These changes allow users to create their own algorithms.

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Prati, Enrico. "Quantum neuromorphic hardware for quantum artificial intelligence." Journal of Physics: Conference Series 880 (August 2017): 012018. http://dx.doi.org/10.1088/1742-6596/880/1/012018.

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李, 肖. "Artificial Intelligence Driven Hardware Trojan Implantation Detection." Computer Science and Application 15, no. 06 (2025): 120–27. https://doi.org/10.12677/csa.2025.156163.

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Kintonova, A. Zh, B. B. Suleimenova, and A. K. Shangytbayeva. "ARTIFICIAL INTELLIGENCE IN EDUCATION." Yessenov Science Journal 48, no. 3 (2024): 303–9. http://dx.doi.org/10.56525/uwkm4465.

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The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.

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Tesi sul tema "Hardware for Artificial Intelligence"

Orozco, Gabriel Mario. "Artificial intelligence opportunities and an end-do-end data-driven solution for predicting hardware failures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104304.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT. Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. In conjunction with the Leaders for Global Operations Program at MIT. Cataloged from PDF version of thesis. Includes bibliographical references (pages 93-96). Dell's target to provide quality products based on reliability, security, and manageability, has driven Dell Inc. to become one of the largest PC suppliers. The recent developments in Artificial Intelligence (AI) combined with a competitive market situation have encouraged Dell to research new opportunities. Al research and breakthroughs have risen in the last years, bringing along revolutionary technologies and companies that are disrupting all businesses. Over 30 potential concepts for Al integration at Dell Inc. were identified and evaluated to select the ones with the highest potential. The top-most concept consisted of preventing in real time the failure of hardware. This concept was investigated using a data science process. Currently, there exist a number of machine learning tools that automate the last stages of the proposed data science process to create predictive models. The utilized tools vary in functionality and evaluation standards, but also provide other services such as data and model storage and visualization options. The proposed solution utilizes the deep feature synthesis algorithm that automatically generates features from problem-specific data. These engineered features boosted predictive model accuracy by an average of 10% for the AUC and up to 250% in recall for test (out of sample) data. The proposed solution estimates an impact exceeding $407M in the first five years for Dell Inc. and all of the involved suppliers. Conservatively, the direct impact on Dell Inc. is particular to batteries under warranty and is expected to surpass $2.7M during the first five years. The conclusions show a high potential for implementation. by Mario Orozco Gabriel. M.B.A. S.M. in Engineering Systems

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Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.

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GRIMALDI, MATTEO. "Hardware-Aware Compression Techniques for Embedded Deep Neural Networks." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2933756.

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Bedi, Abhishek. "A generic platform for the evolution of hardware." Click here to access this resource online, 2009. http://hdl.handle.net/10292/651.

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Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design. The term evolutionary computation involves similar steps as involved in the human evolution. It has been given names in accordance with the electronic technology like, Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP). In evolutionary computing, a configured bit is considered as a human chromosome for a genetic algorithm, which has to be downloaded into hardware. Early evolvable hardware experiments were conducted in simulation and the only elite chromosome was downloaded to the hardware, which was labelled as Extrinsic Hardware. With the invent of Field Programmable Gate Arrays (FPGAs) and Reconfigurable Processing Units (RPUs), it is now possible for the implementation solutions to be fast enough to evaluate a real hardware circuit within an evolutionary computation framework; this is called an Intrinsic Evolvable Hardware. This research has been taken in continuation with project 'Evolvable Hardware' done at Manukau Institute of Technology (MIT). The project was able to manually evolve two simple electronic circuits of NAND and NOR gates in simulation. In relation to the project done at MIT this research focuses on the following: To automate the simulation by using In Circuit Debugging Emulators (IDEs), and to develop a strategy of configuring hardware like an FPGA without the use of their company supplied in circuit debugging emulators, so that the evolution of an intrinsic evolvable hardware could be controlled, and is hardware independent. As mentioned, the research conducted here was able to develop an evolvable hardware friendly Generic Structure which could be used for the development of evolvable hardware. The structure developed was hardware independent and was able to run on various FPGA hardware’s for the purpose of intrinsic evolution. The structure developed used few configuration bits as compared to current evolvable hardware designs.

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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.

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Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.

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Kumar, Sharad Kumar. "Analysis of Machine Learning Modeling Attacks on Ring Oscillator based Hardware Security." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1541759752027838.

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CONTI, DANIELE. "Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711511.

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Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from quantum computing to neuromorphic computing. The present dissertation wants to be a preliminary study for understanding the opportunities enabled by neuromorphic computing based on resistive switching memories. In particular, brain inspires technology and architecture of new generation processors because of its unique properties: parallel and distributed computation, superposition of processing and memory unit, low power consumption, to cite only some of them. Such features make brain particularly efficient and robust against degraded data, further than particularly suitable to process and store in memory new nformation. Despite many research projects and some commercial products are already proposing brain-like computing processors, like spiNNaker or IBM’s Bluenorth, they only mimic the brain functioning with standard Silicon technology, that is inherently serial and distinguish between processing and memory unit. Resistive switching technology on the other hand, would allow to overcome many of these issues, enabling a far better match between biological and artificial neuromorphic computation. Resistive switching are, generally speaking, Metal-Insulator-Metal structures able to change their electrical conductance as a consequence of the history of applied electric signal. In such sense, they behave exactly as synapses do in a biological neural networks. For this reason, resistive switching when modeled as memristor, i.e. memory-resistor, can act as artificial synapses and, moreover, are particularly suitable to be interfaced with artificial Silicon neurons that are designed to replicate the biological behavior when excited with electric pulses. Anyhow, from the technological standpoint, there is still no standard on the design and fabrication of resistive switching, so that multiple structure and materials are investigated. In this dissertation, it is reported an analysis of multiple resistive switching devices, based on various materials, i.e. TiO2, ZnO and HfO, and device architectures, i.e. thin film and nanostructured devices, with the scope of both characterizing and comprehending the physics behind resistive switching phenomena. Furthermore, numerical simulations of artificial spiking neural networks, embedding Silicon neurons and HfO-based resistive switching are designed and performed, in order to give a systematic analysis of the performances reached by this new kind of computing paradigm.

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Imbulgoda, Liyangahawatte Gihan Janith Mendis. "Hardware Implementation and Applications of Deep Belief Networks." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1476707730643462.

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Brink, Stephen Isaac. "Learning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50143.

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The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.

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Libri sul tema "Hardware for Artificial Intelligence"

Mishra, Ashutosh, Jaekwang Cha, Hyunbin Park, and Shiho Kim, eds. Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5.

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Kropf, Thomas. Introduction to Formal Hardware Verification. Springer Berlin Heidelberg, 1999.

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Jovanović, Aleksandar S. Expert Systems in Structural Safety Assessment: Proceedings of an International Course October 2-4, 1989, Stuttgart, FRG. Springer Berlin Heidelberg, 1989.

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Lee, Bang W. Hardware annealing in analog VLSI neurocomputing. Kluwer Academic Publishers, 1991.

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Strous, Leon. Internet of Things. Information Processing in an Increasingly Connected World: First IFIP International Cross-Domain Conference, IFIPIoT 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 18-19, 2018, Revised Selected Papers. Springer Nature, 2019.

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Sood, A. K. Active Perception and Robot Vision. Springer Berlin Heidelberg, 1992.

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Jain, Anil K. Real-Time Object Measurement and Classification. Springer Berlin Heidelberg, 1988.

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Matthias, Scheutz, ed. Computationalism: New directions. MIT Press, 2002.

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Harris, Michael C. Artificial intelligence. Marshall Cavendish Benchmark, 2011.

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Karam, P. Andrew. Artificial intelligence. Chelsea House, 2011.

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Capitoli di libri sul tema "Hardware for Artificial Intelligence"

Chen, Robert H., and Chelsea Chen. "Computing Hardware." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003463542-1.

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Mishra, Ashutosh, Pamul Yadav, and Shiho Kim. "Artificial Intelligence Accelerators." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_1.

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Azoff, Eitan Michael. "AI hardware." In Toward Human-Level Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003507864-12.

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Ahsan, S. M. Mojahidul, Tamzidul Hoque, Md Sakib Hasan, Mrittika Chowdhury, and Anurag Dhungel. "Hardware Accelerators for Artificial Intelligence." In AI-Enabled Electronic Circuit and System Design. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-71436-8_14.

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Yadav, Pamul, Ashutosh Mishra, and Shiho Kim. "Neuromorphic Hardware Accelerators." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_8.

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Lippmann, Bernhard, Matthias Ludwig, and Horst Gieser. "Generating Trust in Hardware through Physical Inspection." In Embedded Artificial Intelligence. River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-5.

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Liu, Yanli, Bochen Guan, Weiyi Li, Qinwen Xu, and Shuxue Quan. "SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning." In Artificial Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_20.

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Burns, Jeff. "The New Era of AI Hardware." In From Artificial Intelligence to Brain Intelligence. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-4.

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Jhung, Junekyo, Ho Suk, Hyungbin Park, and Shiho Kim. "Hardware Accelerators for Autonomous Vehicles." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_9.

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Kim, Jinhyuk, and Shiho Kim. "Hardware Accelerators in Embedded Systems." In Artificial Intelligence and Hardware Accelerators. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22170-5_6.

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Atti di convegni sul tema "Hardware for Artificial Intelligence"

Kostadinov, Atanas N. "Application of Artificial Intelligence in Hardware Description Languages Education." In 2024 XXXIII International Scientific Conference Electronics (ET). IEEE, 2024. http://dx.doi.org/10.1109/et63133.2024.10721535.

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Garate, Eider, Paul San Sebastian, Guillermo Valverde, Alejandra Ruiz, and Meritxell Gómez. "Variational Quantum Regression on NISQ Hardware with Error Mitigation." In 2024 Artificial Intelligence Revolutions (AIR). IEEE, 2024. https://doi.org/10.1109/air63653.2024.00016.

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Sandeep, Dannapaneni, Abhishek Sharma, Mukesh Maurya, Vishnu Vardhan Chary, and Gururaj M. V. "Boosting Solar Load Forecasting Using Artificial Intelligence and Hardware Prototype." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10718115.

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Gül, Fatih. "Memristive Synapses as Building Blocks of Neuromorphic Artificial Intelligence (AI) Hardware." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10711094.

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Mahfuz, Tanzim, Pravin Gaikwad, Tasneem Suha, Swarup Bhunia, and Prabuddha Chakraborty. "SALTY: Explainable Artificial Intelligence Guided Structural Analysis for Hardware Trojan Detection." In 2025 IEEE 43rd VLSI Test Symposium (VTS). IEEE, 2025. https://doi.org/10.1109/vts65138.2025.11022818.

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Li, Zhanguo, Wu Bao, Bo Yu, and Yan Wang. "GPU Energy Efficiency Management Based on Embedded Software and Hardware Systems." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022195.

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Roberge, P. R. "Searching for Corrosion Intelligence." In CORROSION 1999. NACE International, 1999. https://doi.org/10.5006/c1999-99232.

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Abstract The incredible progress in computing power and availability has created a tremendous wealth of information available at the touch of a few buttons. However, such wealth can easily provoke what is commonly described as ‘information overload.’ The massive number of connections produced by a single search of the Web, for example, can greatly overwhelm users of this new technology. The rapidity of Web searches is due to the synergy between progress made in network connectivity protocols, intelligent search strategies and supporting hardware. This paper will attempt to define the basic elements of machine intelligence in the context of corrosion engineering and examine what has been done or could be done to introduce artificial thinking into daily operations.

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Sun, Dongqing, Mingming Yu, Zhen Guo, and Tianling Liu. "Hardware Security Vulnerability Mining Techniques for Intelligent Connected Vehicles." In 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC). IEEE, 2024. http://dx.doi.org/10.1109/aiotc63215.2024.10748330.

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Tian, Hanxi, and Xuehong Lin. "Hardware Acceleration for Transposed Convolution Based on NVDLA." In 2024 3rd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR). IEEE, 2024. https://doi.org/10.1109/aihcir65563.2024.00030.

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Li, Xinchen, Min Xie, Ruixiao Zhao, and Yujie Zhang. "Hardware Acceleration for Detrs Using Systolic Array Architecture." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035038.

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Rapporti di organizzazioni sul tema "Hardware for Artificial Intelligence"

Lohn, Andrew, and Micah Musser. AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress? Center for Security and Emerging Technology, 2022. http://dx.doi.org/10.51593/2021ca009.

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Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.

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Chernavskikh, Vladislav. Nuclear Weapons and Artificial Intelligence: Technological Promises and Practical Realities. Stockholm International Peace Research Institute, 2024. http://dx.doi.org/10.55163/vbqx6088.

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Recent advances in the capabilities of artificial intelligence (AI) have increased state interest in leveraging AI for military purposes. Military integration of advanced AI by nuclear-armed states has the potential to have an impact on elements of their nuclear deterrence architecture such as missile early-warning systems, intelligence, surveillance and reconnaissance (ISR) and nuclear command, control and communications (NC3), as well as related conventional systems. At the same time, a number of technological and logistical factors can potentially limit or slow the adoption of AI in the nuclear domain. Among these are unreliability of output, susceptibility to cyberattacks, lack of good-quality data, and inadequate hardware and an underdeveloped national industrial and technical base. Given the current and relatively early stage of military adoption of advanced AI, the exploration of these factors lays the groundwork for further consideration of the likely realities of integration and of potential transparency measures and governance practices at the AI–nuclear nexus.

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Musser, Micah, Rebecca Gelles, Catherine Aiken, and Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/20210071.

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Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum kernel methods, while analyzing their impact on neural networks, generative models, and reinforcement learning. Hybrid quantum-classical AI architectures, which combine quantum subroutines with classical deep learning models, are examined for their ability to provide computational advantages in optimization and large-scale data processing. Despite the promise of quantum AI, challenges such as qubit noise, error correction, and hardware scalability remain barriers to full-scale implementation. This study provides an in-depth evaluation of quantum-enhanced AI, highlighting existing applications, ongoing research, and future directions in quantum deep learning, autonomous systems, and scientific computing. The findings contribute to the development of scalable quantum machine learning frameworks, offering novel solutions for next-generation AI systems across finance, healthcare, cybersecurity, and robotics. Keywords Quantum machine learning, quantum computing, artificial intelligence, quantum neural networks, quantum kernel methods, hybrid quantum-classical AI, variational quantum algorithms, quantum generative models, reinforcement learning, quantum optimization, quantum advantage, deep learning, quantum circuits, quantum-enhanced AI, quantum deep learning, error correction, quantum-inspired algorithms, quantum annealing, probabilistic computing.

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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.

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anis, sehab. Artificial Intelligence. ResearchHub Technologies, Inc., 2023. http://dx.doi.org/10.55277/researchhub.agwfnyrw.

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Roberts, Kamie. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.600-1.

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Novak, Jr, Simmons Gordon S., Porter Robert F., Kumar Bruce W., Causey Vipin, and Robert L. Artificial Intelligence Project. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230793.

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Guerreiro, Joao, Sergio Rebelo, and Pedro Teles. Regulating Artificial Intelligence. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31921.

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Cwik, Cynthia, Paul Grimm, Maura Grossman, and Toby Walsh. Artificial Intelligence and the Courts: Artificial Intelligence Trustworthiness, and Litigation. American Association for the Advancement of Science, 2022. http://dx.doi.org/10.1126/aaas.adf0786.

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