Harnessing Quantum Cloud Computing: Impacts on Cryptography, AI, and Pharmaceutical Innovation

Anisha Kumari, Ranjan Kumar Behera, Bibhudatta Sahoo

Rajesh Kumar, Nareddy Abhireddy, Ravi Kumar Vankayalapti +1 more

Quantum Cloud Computing is poised to revolutionize various domains by leveraging the principles of quantum mechanics to enhance computational capabilities beyond classical limits. This paper explores the transformative potential of quantum cloud technologies in three critical areas: cryptography, machine learning, and drug discovery. In cryptography, quantum cloud computing enables the development of unbreakable encryption methods through quantum key distribution and post-quantum cryptographic algorithms, ensuring data security in an increasingly interconnected world. In machine learning, quantum algorithms can significantly accelerate data processing and pattern recognition, opening new avenues for complex problem-solving and predictive analytics. Additionally, the application of quantum computing in drug discovery can streamline molecular simulations and optimize drug design processes, leading to faster identification of viable pharmaceutical candidates. By integrating quantum computing with cloud infrastructure, we can democratize access to advanced computational resources, fostering innovation and collaboration across disciplines. This paper discusses the current state of research, potential applications, and the challenges that lie ahead in harnessing quantum cloud computing to drive advancements in these critical fields.

Praveen Chaitanya Jakku, Prasad Sundaramoorthy, Sharath Chandra Kondaparthy +3 more

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.

Ashutosh Kumar, Garima Verma

Ashok Sreerangapuri -

This article presents an innovative framework combining post-quantum cryptography with AI-driven automation to address the imminent threat quantum computing poses to current cloud security systems. With the global cloud computing market projected to reach $1,554.94 billion by 2030 and 94% of enterprises using cloud services, the need for quantum-resistant security solutions is critical. The proposed framework integrates lattice-based cryptography, hash-based signatures, and multivariate cryptography with AI-driven security automation to provide a scalable, adaptable, and future-proof security solution. Initial tests demonstrate the framework's ability to process 5,000 encryption tasks per second while maintaining 99.9% uptime. The article explores the framework's methodology, evaluation results, practical applications across government, finance, and healthcare sectors, and future research directions. By leveraging advanced cryptographic techniques and AI, this framework aims to revolutionize cloud security in the quantum era, ensuring long-term data protection and system resilience.