Exploring The Future Quantum Artificial Intelligence S Promise And

Bonisiwe Shabane

-Jan 2, 2026, 8:27 PM

exploring the future quantum artificial intelligence s promise and

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 The exploration of quantum artificial intelligence (QAI) is gaining momentum as it combines the advancing fields of quantum computing and artificial intelligence. This paper provides an overview of QAI’s current state and future potential, emphasizing both opportunities and challenges in this evolving field. Key insights include significant progress in quantum error correction, which has transitioned quantum computing from a physics challenge to an engineering problem.

The synergy between AI and quantum computing arises from their complementary strengths. AI excels at learning patterns and making predictions from vast datasets, while quantum computing offers the potential to speed up these processes, improve accuracy, and handle more complex datasets. Quantum computing presents potential speedups in otherwise impossible computations but faces challenges such as data loading and hardware limitations. The paper addresses whether quantum computing can assist with generative artificial intelligence (GenAI), concluding that it cannot at present. AI currently aids in advancing quantum computing through hardware design, error correction, and system optimization. Businesses are advised to differentiate between speculative claims and proven applications by focusing on concrete use cases and developing in-house expertise relevant to their industry.

The outlook for QAI suggests its full potential is yet to be realized. Preparing for technological convergence is crucial for maintaining a competitive edge. This paper not only highlights technological advancements leading to QAI but also offers a framework for understanding its implications, challenges, and strategic considerations essential for harnessing its potential. Part of the book series: Studies in Big Data ((volume 179)) Quantum-enhanced artificial intelligence (AI) is an emerging interdisciplinary field that harnesses quantum computing to augment machine learning and decision-making processes. This chapter provides a comprehensive overview of Quantum AI, from fundamental concepts to practical applications.

We begin by outlining the motivations for integrating quantum computing with AI, highlighting how quantum phenomena like superposition and entanglement can potentially accelerate learning and computation. A review of background literature traces the development of quantum machine learning algorithms, including quantum neural networks, quantum support vector machines, and variational quantum eigensolvers. We then examine the theoretical foundations of Quantum AI, explaining how qubits, quantum gates, and hybrid quantum–classical architectures enable novel computational paradigms. The advantages of Quantum AI—such as parallelism and potential speed-ups in complex problem-solving—are weighed against current limitations including hardware noise, scalability challenges, and data encoding overheads. We also discuss ethical and security considerations, especially the implications of quantum-accelerated AI for data privacy and cryptography. Real-world use cases in finance, healthcare, and cybersecurity illustrate the transformative impact of Quantum AI in practice.

Finally, we identify future research directions needed to fully realize the promise of quantum-enhanced decision-making. This chapter aims to equip readers with a clear understanding of the state-of-the-art in Quantum AI, providing technical depth in an accessible manner and laying out the next steps on the frontier of computing... This is a preview of subscription content, log in via an institution to check access. Achuthan, K., Ramanathan, S., Srinivas, S., Raman, R.: Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions. Front. Big Data 7 (2024)

Ahmadi, A.: Quantum computing and AI: the synergy of two revolutionary technologies. Asian J. Electr. Sci. (2023). https://doi.org/10.51983/ajes-2023.12.2.4118

Quantum AI (QAI) refers to the integration of quantum computing and artificial intelligence (AI). The combination of these two cutting-edge technologies has the potential to revolutionize various fields, including data processing, machine learning, optimization, and more. Here’s an overview of what Quantum AI is, how it works, and why it is considered one of the most exciting advancements in the tech world. Quantum AI combines the principles of quantum computing—which leverages the unique properties of quantum mechanics—with AI algorithms to solve complex problems that classical computers struggle to handle. Quantum computing operates on quantum bits, or qubits, which can represent multiple states at once, unlike traditional bits that are binary (either 0 or 1). This allows quantum computers to perform calculations at incredibly fast speeds and handle massive datasets more efficiently than classical computers.

When these quantum capabilities are applied to AI tasks, such as machine learning, data analysis, and decision-making, the potential for breakthroughs in computation and intelligence becomes significant. Quantum computing uses qubits instead of classical bits. Unlike classical computers that process information in binary (0s and 1s), qubits can exist in multiple states at once due to superposition. When combined with entanglement and quantum interference, qubits can process and analyze vast amounts of data in parallel. Ever wondered what would happen if AI’s logic was combined with quantum’s scale? Will a new reality be born?

Or a new intelligence emerge? Read on to find out. A decade ago, Quantum AI felt like the sort of concept that lived in sci-fi novels and research papers nobody read outside physics departments.Today, it’s quietly rewriting how industries operate – from drug discovery... The global Quantum Technology market could reach $97 billion by 2035 (McKinsey). We’re standing at the dawn of a dual revolution – one where AI’s intelligence meets quantum’s unimaginable scale. What emerges next won’t just be faster computation; it will be a new way of thinking.

Quantum artificial intelligence is the fusion of quantum computing and artificial intelligence When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Quantum artificial intelligence is the fusion of quantum computing and artificial intelligence. It is poised to redefine what’s computationally possible and it’s closer to reality than many think. The quantum technology market is rapidly expanding.

Today, it's valued at around $35 billion, but forecasts estimate it could reach $1 trillion by 2030. That kind of explosive growth reflects not only the financial interest in quantum tech, but also its much anticipated capability. In fact, a recent global survey by SAS of 500 business leaders across industries reinforces these projections as it found that interest in Quantum AI is very high, with more than 60% of respondents...

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Baioletti, M.; Fagiolo, F.; Loglisci, C.; Losavio, V.N.; Oddi, A.;

AI. 2025; 6(8):175. Https://doi.org/10.3390/ai6080175 Baioletti, Marco, Fabrizio Fagiolo, Corrado Loglisci,

8: 175. Https://doi.org/10.3390/ai6080175 Baioletti, M., Fagiolo, F., Loglisci, C., Losavio,