Quantum Computing
Quantum computing aims to leverage quantum mechanics to solve problems intractable for classical computers, primarily focusing on optimization and machine learning. Current research heavily emphasizes the development and application of quantum machine learning algorithms, including variational quantum circuits, quantum neural networks, and quantum kernel methods, often integrated with classical techniques in hybrid approaches. This field holds significant potential for accelerating scientific discovery and impacting various applications, from drug discovery and materials science to financial modeling and medical diagnostics, although challenges in hardware limitations and algorithm design remain.
Papers
AQMLator -- An Auto Quantum Machine Learning E-Platform
Tomasz Rybotycki, Piotr Gawron
Exploring LLM-Driven Explanations for Quantum Algorithms
Giordano d'Aloisio, Sophie Fortz, Carol Hanna, Daniel Fortunato, Avner Bensoussan, Eñaut Mendiluze Usandizaga, Federica Sarro
QuForge: A Library for Qudits Simulation
Tiago de Souza Farias, Lucas Friedrich, Jonas Maziero
Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components
Peiyong Wang, Casey. R. Myers, Lloyd C. L. Hollenberg, Udaya Parampalli