1 code implementation • 19 May 2023 • Nilin Abrahamsen, Jiahao Yao
We propose two procedures to create painting styles using models trained only on natural images, providing objective proof that the model is not plagiarizing human art styles.
no code implementations • 30 Mar 2022 • Jiahao Yao, Haoya Li, Marin Bukov, Lin Lin, Lexing Ying
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices.
no code implementations • 12 Dec 2020 • Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, Marin Bukov
Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems.
no code implementations • 7 Oct 2020 • Jiahao Yao, Lin Lin, Marin Bukov
We propose a generalized QAOA called CD-QAOA, which is inspired by the counterdiabatic driving procedure, designed for quantum many-body systems and optimized using a reinforcement learning (RL) approach.
no code implementations • 4 Feb 2020 • Jiahao Yao, Marin Bukov, Lin Lin
Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control.
no code implementations • ICLR 2020 • Michael Luo, Jiahao Yao, Richard Liaw, Eric Liang, Ion Stoica
To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.