Why are quantum computing technologies the future of artificial intelligence?
DOI:
https://doi.org/10.71310/pcam.2_64.2025.08Keywords:
quantum computing, artificial intelligence, quantum AI, innovative strategies, business innovation, medicineAbstract
Quantum technologies are transforming industries by harnessing the principles of quantum mechanics. These innovations promise to revolutionize computing, communi cations, sensing, cryptography, and healthcare, offering solutions previously considered unattainable. Quantum computing is changing the way complex problems are solved: un like classical computers that use bits to represent information in binary code, quantum computers exploit the laws of quantum physics to perform calculations beyond the reach of even the most advanced classical systems. Quantum algorithms can efficiently solve logistical challenges, optimize global supply chains, accelerate risk analysis, and enhance decision-making in complex financial models. Quantum computers enable unprecedentedly fast simulation of molecular interactions, expediting drug discovery, enabling per sonalized medicine, and elevating disease diagnostics to a new level. All these advances lay the foundation for the future development of artificial intelligence, the prospects of which are discussed in this paper.
References
https://www.bbva.com/en/innovation/technology-trends-2025-from-quantum-computing to-ai-agents/
https://www.gartner.es/es/articulos/principales-tendencias-tecnologicas-2025
https://www.capgemini.com/insights/research-institute/
https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html
https://www.mckinsey.com/mgi/our-research/the-next-big-arenas-of-competition
https://www.accenture.com/us-en/insights/song/accenture-life-trends
https://www.businessinsider.es/tecnologia/amazon-microsoft-apuestan-big-tech-energia nuclear-impulsar-ia-1412560
https://www.iaea.org/newscenter/news/what-are-small-modular-reactors-smrs
How M.L., Cheah S.M.Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation AI 2024, 5, 290–323.https://doi.org/10.3390/ai5010015
Neukart F., Compostella G., Seidel C., Von Dollen D., Yarkoni S., Parney B. Traffic Flow Optimization Using a Quantum Annealer. Front. ICT 2017. 4, 29.
Batra K., Zorn K.M., Foil D.H., Minerali E., Gawriljuk V.O., Lane T.R., Ekins S. Quantum Machine Learning Algorithms for Drug Discovery Applications. J. Chem. Inf. Model. 2021. 61,– P. 2641–2647.
Or´ us R., Mugel S., Lizaso E. Quantum Computing for Finance: Overview and Prospects. Rev. Phys.– 2019. 4, 100028.
Veis L., Pittner J. Quantum Computing Applied to Calculations of Molecular Energies: CH2 Benchmark. J. Chem. Phys.– 2010. 133,194106.
Brynjolfsson E., McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, 1st ed.; W. W.Norton & Company: New York, NY, USA, 2014. ISBN 978-0-393-23935-5.
Preskill J. Quantum Computing in the NISQ Era and Beyond. Quantum– 2018. 2, 79.
Amin M.H., Andriyash E., Rolfe J., Kulchytskyy B., Melko R. Quantum Boltzmann Machine. Phys. Rev. X– 2018. 8, 021050
Cao Y., Romero J., Olson J.P., Degroote M., Johnson P.D., Kieferova M., Kivlichan I.D., Menke T., Peropadre B., Sawaya N.P.D., et al. Quantum Chemistry in the Age of Quantum Computing. Chem. Rev.– 2019. 119,– P. 10856–10915.
Farhi E., Goldstone J., Gutmann S., Zhou L. The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size. Quantum– 2022. 6, 759
Ullah and B. Garcia-Zapirain "Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications,"in IEEE Access, vol. 12, P. 11423-11450.– 2024. doi: 10.1109/ACCESS.2024.3353461.
Chow J.C.L. Quantum Computing in Medicine Med. Sci.– 2024. 12, 67.
Doga H., Bose A., Sahin M.E., Bettencourt-Silva J., Pham A., Kim E., Andress A., Saxena S., Parida L., Robertus J.L., et al. How can quantum computing be applied in clinical trial design and optimization? Trends Pharmacol. Sci.– 2024. 45,– P. 880–891.
Sharma P. Quantum Computing in Drug Design: Enhancing Precision and Efficiency in Pharmaceutical Development. Sage Sci.Rev. Appl. Mach. Learn.– 2024. 7,– P. 1–9.
Niraula D., Jamaluddin J., Matuszak M.M., Haken R.K., Naqa I.E. Quantum deep reinforcement learning for clinical decision support in oncology: Application to adaptive radiotherapy. Sci. Rep.– 2021. 11, 23545.
Enad H.G., Mohammed M.A. A review on artificial intelligence and quantum machine learning for heart disease diagnosis:Current techniques, challenges and issues, recent developments, and future directions. Fusion Pract. Appl. (FPA)– 2023. 11,– P. 8–25.
Ur Rasool R., Ahmad H.F., Rafique W., Qayyum A., Qadir J., Anwar Z. Quantum computing for healthcare: A review. Future Internet 2023. 15, 94.
Flother F.F. The state of quantum computing applications in health and medicine. Res. Dir. Quantum Technol.– 2023. 1, e10.
Wang P.H., Chen J.H., Yang Y.Y., Lee C., Tseng Y.J. Recent advances in quantum computing for drug discovery and development.IEEE Nanotechnol. Mag.– 2023. 17,– P. 26–30.
Cao Y., Romero J., Olson J.P., Degroote M., Johnson P.D., Kieferova M., Kivlichan I.D., Menke T., Peropadre B., Sawaya N.P. et al. Quantum chemistry in the age of quantum computing. Chem. Rev.– 2019. 119,– P. 10856–10915.
Cao Y., Romero J., Aspuru-Guzik A. Potential of quantum computing for drug discovery. IBM J. Res. Dev.– 2018. 62, 6:1–6:20.
Unkefer H., Granstra C. Accenture Labs and 1QBit Work with Biogen to Apply Quantum Computing to Accelerate Drug Discovery.– 2017. Available online: https://newsroom.accenture.com/news/2017/accenture-labs-and-1qbit-work-with biogento-apply-quantum-сomputing-to-accelerate-drug-discovery
Constantino A.K. Moderna Teams up with IBM to Put A.I., Quantum Computing to Work on mRNA Technology Used in Vaccines.– 2023. Available online: https://www.cnbc.com/2023/04/20/moderna-and-ibm-to-use-ai-quantum-computing onmrna-vaccines.html
Zinner M., Dahlhausen F., Boehme P., Ehlers J., Bieske L., Fehring L. Quantum computing’s potential for drug discovery: Early stage industry dynamics. Drug Discov. Today– 2021. 26,– P. 1680–1688.
Gepp A., Stocks P. A review of procedures to evolve quantum algorithms. Genet. Program. Evolvable Mach.– 2009. 10,– P. 181–228.
Vashisth S., Dhall I., Aggarwal G. Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis. J. Intell. Syst.– 2021. 30,– P. 998–1013.
A Quantum Leap: Mapping DNA Diversity with Quantum Computing. 2024. Available online: https://www.maths.cam.ac.uk/features/quantum-leap-mapping-dna-diversity quantum-omputing
Abbas A., Sutter D., Zoufal C., Lucchi A., Figalli A.,Woerner S. The power of quantum neural networks. Nat. Comput. Sci.– 2021. 1,– P. 403–409.
Ding C., Bao T.Y., Huang H.L. Quantum-inspired support vector machine. IEEE Trans. Neural Netw. Learn. Syst.– 2021. 33,– P. 7210–7222.
Alhudhaif A.A. A novel approach to recognition of Alzheimer’s and Parkinson’s diseases: Random subspace ensemble classifier based on deep hybrid features with a super-resolution image. PeerJ Comput. Sci. 2024. 10, e1862.
Adebayo P., Basaky F., Osaghae E. Developing a Model for Predicting Lung Cancer Using Variational Quantum-Classical Algorithm: A Survey. J. Appl. Artif. Intell.– 2022. 3,– P. 47–60.
Hu F., Wang B.N., Wang N., Wang C. Quantum machine learning with D-wave quantum computer. Quantum Eng.– 2019. 1, e12.
Wang X. Quantum-enhanced MRI Sensitivity: Dissolution-dynamic Nuclear and Parahydrogen-induced Polarization. Highlights Sci. Eng. Technol.– 2023. 38,– P. 423–430.
Aslam N., Zhou H., Urbach E.K., Turner M.J., Walsworth R.L., Lukin M.D., Park H. Quantum sensors for biomedical applications. Nat. Rev. Phys.– 2023. 5,– P. 157–169.
Cheng B., Deng X.H., Gu X., He Y., Hu G., Huang P., Li J., Lin B.C., Lu D., Lu Y., et al. Noisy termediate-scale quantum computers. Front. Phys.– 2023. 18, 21308.
Knill E. Scalable quantum computing in the presence of large detected-error rates. Phys. Rev. A—At. Mol. Opt. Phys. 2005. 71, 042322.
Humble T.S., McCaskey A., Lyakh D.I., Gowrishankar M., Frisch A., Monz T. Quantum computers for high-performance computing. IEEE Micro– 2021. 41,– P. 15–23.
Kumar, A., Bhushan, B., Shriti, S., Nand, P. Quantum computing for health care: A review on implementation trends and recent advances. In Multimedia Technologies in the Internet of Things Environment, Springer: Singapore,– 2022.– Volume 3,– P. 23–40.
Roffe J. Quantum error correction: An introductory guide. Contemp. Phys.– 2019. 60,– P. 226–245.
Gottesman D. An introduction to quantum error correction and fault-tolerant quantum computation. In Quantum Information Science and Its Contributions to Mathematics, Proceedings of the Symposia in Applied Mathematics, American Mathematical Society: Washington, DC, USA, 2010. Volume 68,– P. 13–58.
Stern A., Lindner N.H. Topological quantum computation—From basic concepts to first experiments. Science– 2013. 339,– P. 1179–1184.
Lubinski T., Granade C., Anderson A., Geller A., Roetteler M., Petrenko A., Heim B. Advancing hybrid quantum–classical computation with real-time execution. Front. Phys. 2022. 10, 940293.
Farhi E., Goldstone J., Gutmann S. A quantum approximate optimization algorithm. arXiv– 2014. arXiv:1411.4028.
Jeyaraman N., Jeyaraman M., Yadav S., Ramasubramanian S., Balaji S. Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment. Cureus– 2024. 16, e67486.
Davids J., Lidstromer N., Ashrafian H. Artificial intelligence in medicine using quantum computing in the future of healthcare. In Artificial Intelligence in Medicine, Springer International Publishing: Cham, Switzerland,– 2022.– P. 423–446.
