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Found 6 papers, 1 papers with code
Inventing art styles with no artistic training data
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.
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices.
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
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.
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
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.
Policy Gradient based Quantum Approximate Optimization Algorithm
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.
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.
