2 citations · 6 across the 8 of their papers we have counts for
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quant-ph2023★ 1 cited
Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions
Ioana Moflic, Alexandru Paler
Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function that decides what is correct and what is wron…
quant-ph2023
Cost Explosion for Efficient Reinforcement Learning Optimisation of Quantum Circuits
Ioana Moflic, Alexandru Paler
Large scale optimisation of quantum circuits is a computationally challenging problem. Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum…
quant-ph2023★ 2 cited
Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation
Ioana Moflic, Vikas Garg, Alexandru Paler
Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when consider…