Session

DescriptionThe session discusses game-changing applications of quantum computing in finance, community detection, computational advantages of quantum machine learning over classical models, quantum cryptanalysis, and developing distributed quantum computing for real-world applications. The first paper solves portfolio optimization problems in finance with the help of a novel design of an HHL quantum algorithm. Community detection is an important problem in network analysis; the second paper demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in large-scale graph data. The third paper shows that hybrid quantum neural networks provide a more scalable and resource-efficient solution over purely classical models, positioning them as a promising alternative for tackling complex computational problems. The fourth and fifth papers discuss novel quantum arithmetic circuits for quantum cryptanalysis with the help of measurement-based uncomputation and windowed arithmetic. Distributed quantum computing (DQC) offers a promising pathway for scaling up quantum computing; therefore, the sixth paper discusses hardware-software co-design for DQC systems, paving the way for more practical and efficient implementations for real-world applications.