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
Quantum Architecture Search: A Survey
Darya Martyniuk, Johannes Jung, Adrian Paschke
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
Michael Kölle, Timo Witter, Tobias Rohe, Gerhard Stenzel, Philipp Altmann, Thomas Gabor
Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness
Chirag Wadhwa, Mina Doosti