Quantum Circuit
Quantum circuits are the fundamental building blocks of quantum computation, aiming to leverage quantum phenomena for computational advantage. Current research heavily focuses on optimizing circuit design for efficiency and robustness, exploring various architectures like variational quantum circuits (VQCs) and quantum neural networks (QNNs), often employing reinforcement learning and other advanced optimization techniques to mitigate issues like barren plateaus and noise. These efforts are crucial for advancing the capabilities of near-term quantum devices and enabling practical applications in areas such as quantum machine learning and optimization problems. The development of efficient and reliable quantum circuits is a key step towards realizing the full potential of quantum computing.
Papers
Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
Abhishek Setty, Rasul Abdusalamov, Felix Motzoi
Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing
M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, Zoë Holmes
Data is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteins
Raghav Jumade, Nicolas PD Sawaya
Expressive variational quantum circuits provide inherent privacy in federated learning
Niraj Kumar, Jamie Heredge, Changhao Li, Shaltiel Eloul, Shree Hari Sureshbabu, Marco Pistoia