The AI–quantum computing mash-up: will it revolutionize science?

Maha A. Metawei, Hesham E. ElDeeb, Salwa M. Nassar +1 more

Sandeep Chouhan, Ramandeep Sandhu, Chandani Bhasin

The union of artificial intelligence (AI) and quantum computing (QC) is set to revolutionize computational power, ushering in a new era of technology. While machine learning (ML) revolutionized industries with the capability of making intelligent predictions based on data processing, it is circumscribed by the constraints of classical computer science. QC, which can process massive data concurrently with the aid of quantum bits (qubits), solves these limitations. Quantum machine learning (QML) involves QC but adds traditional ML algorithms to make the processing of complex data sets faster and efficient. Taking advantage of quantum properties like superposition and entanglement, QML can advance model training, speed up pattern recognition, and solve large-scale issues that classical ML cannot do or is impossible. This technology will see phenomenal advances in health care, cryptography, financial analysis, and drug development. The combination of AI and quantum computing will dramatically speed up the scope and scale of ML operations and open up new avenues to accomplish things in ways that were previously impossible. Although challenges exist in creating stable quantum systems and scalable algorithms, the pairing of AI, ML, and QML is a watershed moment in computational science.

Jaspreet Singh, Paramjot Kaur Sarao, Manish Sharma +3 more

Cloud Quantum Computing (CQC) is a paradigm shift in computer science that has the potential to revolutionize Artificial Intelligence (AI) by utilizing the computational capabilities of quantum mechanics. The primary objective of this chapter is to examine the architectural framework of CQC, assess its impact on AIdriven applications, and analyze key challenges and innovations shaping its development. It focuses on critical aspects of CQC, such as qubit fidelity, quantum error correction, and hybrid quantum-classical models. The chapter also incorporates the practical uses of CQC, highlighting its benefits in areas such as optimization, cryptography, and machine learning. The key findings of this chapter are that although CQC offers significant advantages in computational efficiency and problem-solving capabilities, it is still hindered by hardware limitations, noise interference, and algorithmic complexity. Innovations in error correction techniques and hybrid models are crucial for overcoming these barriers. By leveraging the power of quantum computing through cloud platforms, CQC has the potential to revolutionize AI and computational science.

Fatma Foad Ashrif, Rami Ahmad, Faiza A. M. Mansour

Alexey N. Pyrkov, Alex Aliper, Dmitry S. Bezrukov +3 more

Abstract Having made significant advancements in understanding living organisms at various levels such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now shifting towards integrating these components into the bigger picture to understand their collective behavior. Such a shift of perspective requires a general conceptual framework for understanding complexity in life sciences which is currently elusive, a transition being facilitated by large‐scale data collection, unprecedented computational power, and new analytical tools. In recent years, life sciences have been revolutionized with AI methods, and quantum computing is touted to be the next most significant leap in technology. Here, we provide a theoretical framework to orient researchers around key concepts of how quantum computing can be integrated into the study of the hierarchical complexity of living organisms and discuss recent advances in quantum computing for life sciences. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Quantum Computing > Algorithms Structure and Mechanism > Computational Biochemistry and Biophysics

Giovanni Acampora