Recent Advances in Quantum Computing - Emerging Algorithms, Architectures, and Applications

Quincy Mccarthy

Ishmeen Kaur Garewal, Chaitanya Vijaykumar Mahamuni, Shruti Jha

Quantum computing is at the cutting edge of technological innovation, with the potential to transform a wide array of scientific and practical fields. This literature survey addresses the critical need for a structured understanding of recent advancements and identifies research gaps. It comprehensively reviews recent advancements in quantum computing, emphasizing non-standard architectures, applications in material sciences, and the integration of quantum and classical machine learning models. The survey also examines quantum algorithms for real-time systems, explores cryptographic protocols beyond Shor's algorithm, and discusses resource management in cloud computing. Key findings highlight significant progress in algorithm efficiency, practical applications in 2D materials and topological insulators, and the hybridization of quantum and classical models. Challenges such as scalability, resource optimization, and ethical concerns still remain. This paper guides future research to focus on optimizing algorithms, efficient resource allocation, and enhancing security measures. This work synthesizes current knowledge, guiding future research efforts and contributing to the advancement of the quantum computing research community.

Mrs. Sri Lavanya Sajja Dr. L. Sherly Puspha Annabel Dr. Chatrapathy K

Samuel Yen-Chi Chen, Zhiding Liang

Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.

Siddharth Kote, Dnyaneshwar Kshirsagar, Raj B. Kulkarni +3 more

Haozhen Situ, Zhimin He, Lvzhou Li

Variational quantum algorithms (VQAs) constitute a prominent paradigm for exploring the capabilities of near-term quantum computers. As the effectiveness of VQAs depends heavily on the design of variational quantum circuits, Quantum Architecture Search (QAS) has emerged as a critical research area to automate the discovery of high-performing circuit structures. This paper reviews key advancements in current research on QAS, including its core concepts, representative methodologies, applications, and emerging trends. The content is structured to ensure broad accessibility for a diverse audience of researchers while preserving the core principles of complex methodologies. In addition, we discuss remaining challenges and suggest potential research directions to offer perspectives on future exploration in this rapidly evolving field.