Quantum Computing And Ai Perspectives On Advanced Automation In

Bonisiwe Shabane

-Jan 2, 2026, 8:28 PM

quantum computing and ai perspectives on advanced automation in

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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) Nature Communications volume 16, Article number: 10829 (2025) Cite this article Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI’s data-driven learning capabilities, and in fact, many of QC’s biggest scaling challenges may ultimately rest on developments in AI.

However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space. Quantum computing (QC) has the potential to impact every domain of science and industry, but it has become increasingly clear that delivering on this promise rests on tightly integrating fault-tolerant quantum hardware with accelerated... Such large-scale quantum supercomputers form a heterogeneous architecture with the ability to solve certain otherwise intractable problems. Many of these problems, such as chemical simulation or optimization, are projected to have significant scientific, economic and societal impact1.

However, transitioning hardware from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing (FTQC) faces a number of challenges. Though recent quantum error correction (QEC) demonstrations have been performed2,3, all popular qubit modalities suffer from hardware noise, preventing the below-threshold operation needed to perform fault-tolerant computations. But even qubits performing below threshold face scaling obstacles. FTQC is demanding and necessitates more resourceful QEC codes, faster decoder algorithms, and carefully designed qubit architectures. Both QC hardware research and current quantum algorithms also require further development with explorations of more resource-efficient techniques, having the potential to dramatically shorten the roadmap to useful quantum applications. Though high-performance computing (HPC)4,5,6, and in particular, accelerated GPU computing7,8, already drives QC research through circuit and hardware simulations, the rise of generative artificial intelligence (AI) paradigms9 has only just begun.

Foundational AI models10, characterized by their broad training data and ability to adapt to a wide array of applications, are emerging as an extremely effective way to leverage accelerated computing for QC. While the architecture landscape of these models is diverse, transformer models11 have proven particularly powerful, and especially popularized by OpenAI’s generative pre-trained transformer (GPT) models12,13. There is already a strong precedent for these models being applied to technical yet pragmatic tasks in other fields, ranging from biomedical engineering14 to materials science15. Bringing the deep utility and broad applicability of such models to bear on the problems facing QC is a key goal of this review. The “second quantum revolution” was central to the Fellowship, focusing on quantum logic's impact across disciplines. The research bridged quantum physics with machine learning, AI, cognitive science, and biomedical diagnostics.

Key outcomes included new algorithms, quantum computer benchmarking, and interdisciplinary dialogue through seminars and collaboration with the Leibniz Supercomputing Centre. Prof. Roberto Giuntini (University of Cagliari), Alumnus Philosopher in Residence (funded by the TÜV SÜD Foundation and as part of the Excellence Strategy of the federal and state governments) | Hosts: Prof. Hans Bungartz, Prof. Stefania Centrone, Prof. Klaus Mainzer (TUM)

Over the course of six visits to the TUM-IAS, spanning more than six months in total, I investigated the concept of “quantumness,” a defining feature of quantum systems characterized by phenomena such as superposition,... The project aimed to apply quantum logic to disciplines beyond physics, including machine learning, data science, and biomedical diagnostics. The research expanded to include the intersection of artificial intelligence (AI) and quantum sciences. This included exploring how large language models (LLMs) could benefit from quantum principles, providing a framework for advancing quantum-inspired computational tools. The seminar series (Quantumness: From Logic to Engineering and Back), organized with my Hosts, was a cornerstone of the Fellowship, fostering rich interdisciplinary dialogue. The proceedings of these seminars will be published, edited by H.

Bungartz, S. Centrone, R. Giuntini, M. Molls and K. Mainzer, in the prestigious “Synthese Library” series under the title Quantum Logic and Beyond.Key seminars included:First Quantum Afternoon (December 6, 2023): Prof. Giuseppe Sergioli presented “Quantum State Discrimination for Supervised Classification,” showcasing quantum logic’s application in machine learning.

Prof. Christian Mendl provided insights into quantum simulations for analyzing complex systems.Computing Systems: Mathematical Entities or Physical Objects? (March 15, 2024): Prof. Marco Giunti explored the dual nature of computational systems and its philosophical implications for quantum research. Second Quantum Afternoon (May 16, 2024): Profs. Robert Wille and Klaus Mainzer examined automation in quantum computing design and the interplay between AI and quantum technologies.

Received 2025 Feb 25; Accepted 2025 Oct 24; Collection date 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit... The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from... To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas.

Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI’s data-driven learning capabilities, and in fact, many of QC’s biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space. Subject terms: Quantum information, Quantum simulation

Quantum computing devices of increasing complexity are becoming more and more reliant on automatised tools for design, optimization and operation. In this Review, the authors discuss recent developments in “AI for quantum", from hardware design and control, to circuit compiling, quantum error correction and postprocessing, and discuss future potential of quantum accelerated supercomputing, where... Baioletti, M.; Fagiolo, F.; Loglisci, C.; Losavio, V.N.; Oddi, A.; Rasconi, R.; Gentili, P.L. Quantum Artificial Intelligence: Some Strategies and Perspectives. AI 2025, 6, 175. https://doi.org/10.3390/ai6080175

Baioletti M, Fagiolo F, Loglisci C, Losavio VN, Oddi A, Rasconi R, Gentili PL. Quantum Artificial Intelligence: Some Strategies and Perspectives. AI. 2025; 6(8):175. https://doi.org/10.3390/ai6080175 Baioletti, Marco, Fabrizio Fagiolo, Corrado Loglisci, Vito Nicola Losavio, Angelo Oddi, Riccardo Rasconi, and Pier Luigi Gentili.

2025. "Quantum Artificial Intelligence: Some Strategies and Perspectives" AI 6, no. 8: 175. https://doi.org/10.3390/ai6080175 Baioletti, M., Fagiolo, F., Loglisci, C., Losavio, V. N., Oddi, A., Rasconi, R., & Gentili, P.

L. (2025). Quantum Artificial Intelligence: Some Strategies and Perspectives. AI, 6(8), 175. https://doi.org/10.3390/ai6080175

Quantum Computing And Ai Perspectives On Advanced Automation In

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