83 citations · 137 across the 7 of their papers we have counts for
7 papers
Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
Zhiyan Ding, Taehee Ko, Jiahao Yao +2
Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learn…
Inventing art styles with no artistic training data
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. In t…
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Jiahao Yao, Haoya Li, Marin Bukov +2
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only…
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
Jiahao Yao, Paul Köttering, Hans Gundlach +2
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…
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
Jiahao Yao, Lin Lin, Marin Bukov
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired by the coun…
Policy Gradient based Quantum Approximate Optimization Algorithm
Jiahao Yao, Marin Bukov, Lin Lin
The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz…
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
Michael Luo, Jiahao Yao, Richard Liaw +2
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinfor…