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
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse
Elisha Siddiqui Matekole, Esther Ye, Ramya Iyer, Samuel Yen-Chi Chen
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
Massimiliano Incudini, Michele Grossi, Antonio Mandarino, Sofia Vallecorsa, Alessandra Di Pierro, David Windridge
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
Michael Kölle, Alessandro Giovagnoli, Jonas Stein, Maximilian Balthasar Mansky, Julian Hager, Claudia Linnhoff-Popien
Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations
Merlin A. Nau, A. Hans Vija, Wesley Gohn, Maximilian P. Reymann, Andreas K. Maier
Quantum median filter for Total Variation image denoising
Simone De Santis, Damiana Lazzaro, Riccardo Mengoni, Serena Morigi