University of Kwazulu-Natal

Quantum Computing And Ai Ki Künstliche Intelligenz Springer

Bonisiwe Shabane
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quantum computing and ai ki künstliche intelligenz springer

You have full access to this open access article Avoid common mistakes on your manuscript. Quantum computing is currently in an important transitional stage: While quantum computers nowadays have reached a size and performance that cannot or only with restrictions be classically simulated, it remains to be investigated for... This defined an ideal entry point to look beyond the already proven acceleration of a few certain classical computing tasks such as unstructured search and prime factorisation by quantum computers with a keen focus... In fact, Quantum Artificial Intelligence (QAI) aims to combine both worlds to their respective advantage. In other words, QAI research is concerned with the use of quantum computing for addressing computationally hard problems in AI, and vice versa, the use of AI to tackle challenges of building and operating...

Of particular interest are the development of direct quantum or hybrid quantum-classical algorithms for problem solving and the investigation of their potential advantage over their best classical counterparts. Within AI, quantum AI is not just limited to quantum machine learning but also encompasses quantum planning and scheduling, quantum natural language processing, quantum computer vision, and quantum multi-agent systems. In the past decade, an impressive progress was made in both directions of QAI. Research on quantum-supported solutions of selected hard optimization problems in AI provided insights on the potential of quantum utility with use cases in various domains such as manufacturing, automated driving, transport and logistics, finance,... By 2030, the global market of quantum AI applications in general is actually estimated to be worth around eighteen billions of USD [1]. On the other hand, quantum computing can benefit from the use of AI methods, particularly from machine learning, for optimizing the control, performance and calibration of quantum computational devices.

There is great hope: even if quantum computers initially “merely” improve the simulations of quantum mechanical processes, they could, among other things, help to train AI systems more efficiently in the future. The first small quantum computers are already commercially available. But what potential do they actually have today? Classical computers can do a lot. But their capabilities have limits, for example, when it comes to simulating quantum mechanical systems such as molecules. Enter quantum computers: machines that directly rely on quantum principles for their calculations.

The hope is that they will not only compute much more efficiently, but also push the boundaries of what is possible. This matters for developing new drugs and materials, optimizing logistics and production, solving differential equations (for example in fluid dynamics), and advancing machine learning. The potential economic impact is enormous: a Boston Consulting Group study projects up to US $ 80 billion in value from drug development and as much as US $ 100 billion from logistics over... What actually distinguishes quantum computing from classical computers? While calculations on a classical computer are based on bits, which can take on the values 0 or 1, a quantum computer is based on qubits – they can be in a superposition of... This makes the operations fundamentally different.

The power of quantum computers lies in quantum effects —superposition, entanglement, and interference. “Interference allows states to be amplified or attenuated, as is also known from classical waves,” writes Jeanette Miriam Lorenz, Head of the Quantum Computing Department at Fraunhofer IKS, in the book “Künstliche Intelligenz und... Stand, Nutzung und Herausforderungen der KI”. Superposition, on the other hand, enables quantum computers to apply computations to multiple states simultaneously, naturally parallelizing the calculations. Further advantages offered by quantum computers: Calculations are less complex and therefore likely to be more energy-efficient – an attractive prospect for high-performance computing, known for its substantial energy demands. Moreover, they are expected to enhance generalization, increase algorithmic processing power, and facilitate better separation of data points.

However, while these benefits are intriguing, they remain largely theoretical for now, as they hinge on the existence of a perfect quantum computer free from errors and interference. This article is based on the contribution by PD Dr. habil. Jeanette Miriam Lorenz, Head of Department at Fraunhofer IKS, in the book “Artificial Intelligence and Us. Status, Use, and Challenges of AI.” The editors are Frank Schmiedchen, Alexander von Gernler, Martina Hafner, and Klaus Peter Kratzer.The book is available for free download at https://link.springer.com/book.... You have full access to this open access article

Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of... Avoid common mistakes on your manuscript. It is known that quantum computing can simulate and even go beyond classical computing in terms of computational speedup in theory [3, 130, 153] for quite some time. But initial versions of real quantum computing hardware and frameworks for quantum programming became available only in about the past decade.

Even the Quantum Internet of networked quantum computers and with secure quantum communication channels, long time considered as mere science fiction, is on its way with early stage prototypes available [26, 33, 59]. On the other hand, artificial intelligence (AI) [156] is commonly considered as one of the most disruptive key technologies of our time for industry and business, our private and social life, notwithstanding the challenges... Quantum AI (QAI) as intersection of quantum computing and AI with subfields in relation to AI each covering both directions A Correction to this article was published on 11 October 2024 This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout.

A Correction to this paper has been published: https://doi.org/10.1007/s13218-024-00875-4 Bluvstein D et al (2024) Logical quantum processor based on reconfigurable atom arrays. Nature, 626(7997). Also see: http://phys.org/news/2023-12-logical-qubits-quantum-errors.html. Accessed 13 Sept 2024 Part of the book series: Studies in Computational Intelligence ((volume 1229))

Included in the following conference series: Quantum Artificial Intelligence (QAI) is the convergence of quantum computing and artificial intelligence, offering unprecedented computational capabilities. Quantum AI is capable of revolutionizing machine learning, optimization, and decision-making by leveraging quantum aspects such as entanglement, superposition, and quantum parallelism. However, significant challenges remain in realizing its full potential. This paper investigates how quantum algorithms enhance AI efficiency, the optimization techniques applicable to AI, the key challenges hindering adoption, and the future advancements required for real-world implementation. By addressing these aspects, this study explores the fundamental challenges of Quantum AI, including hardware limitations, algorithmic complexity, workforce shortages, and the lack of standardized development frameworks.

It also highlights key areas for future research, including advancements in quantum hardware, optimization of quantum algorithms, expansion of workforce training programs, and standardization of development tools. As Quantum AI matures, it is expected to drive breakthroughs in multiple industries, such as cybersecurity, finance, healthcare, and industrial optimization, paving the way for next-generation AI-driven innovation. This is a preview of subscription content, log in via an institution to check access. Ahmadi.: Quantum computing and artificial intelligence: the synergy of two revolutionary technologies. Asian J. Electr.

Sci. 15–27 (July 2023) You have full access to this open access article Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to scale quantum machine learning (QML) to industrial-level applications. This paper investigates the scalability and noise resilience of quantum generative learning applications.

We consider the training performance in the presence of statistical noise due to finite-shot noise statistics and quantum noise due to decoherence to analyze the scalability of QML methods. We employ rigorous benchmarking techniques to track progress and identify challenges in scaling QML algorithms, and show how characterization of QML systems can be accelerated, simplified, and made reproducible when the QUARK framework is... We show that QGANs are not as affected by the curse of dimensionality as QCBMs and to which extent QCBMs are resilient to noise. Avoid common mistakes on your manuscript. Systematic evaluation of quantum processors and algorithms through benchmarking offers valuable insights into the current capabilities and future potential of available quantum processing units [1,2,3,4,5,6,7,8,9]. However, benchmarking quantum computing is far from a straightforward task.

The field is characterized by a diversity of technologies [10], each with unique requirements for precise and meaningful assessment. As a result, current benchmarks often focus on specific aspects of the technology, which can sometimes lead to an incomplete picture of the end-to-end performance of quantum computing. The Quantum computing Application benchmark (QUARK) framework [8] was explicitly developed for challenges of application-oriented quantum computing. QUARK’s benchmarking approach ensures a comprehensive evaluation, covering the entire benchmarking pipeline from hardware to algorithmic design for the problems under investigation. Its versatility and modular implementation are central to QUARK, allowing for component expansion and customization. Additionally, it hosts benchmarks from the domain of optimization [8] and machine learning [11].

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