Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals
Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals
The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first …