Advanced example: Reinforcement Learning for Quantum Feedback and Control

Here is an example from our own group which combines many of the advanced techniques that are explained in the lectures (in particular, reinforcement learning and recurrent networks). Our setting is simple: you are given a set of a few qubits, with one of them initialized in an arbitrary (unknown) initial quantum state. There is decoherence due to the unavoidable noise of the environment. How do you best preserve the quantum state? Using reinforcement learning, the neural network discovers from scratch quantum error correction strategies. These involve gates (e.g. CNOT) that manipulate and entangle the qubits. The strategies also involve measurements, and the subsequent actions will depend on the measurement outcomes. This challenge demonstrates very nicely the power of reinforcement learning: the same program can be applied to many different physical scenarios.

Read the paper on arXiv: 1802.05267

(“Reinforcement Learning with Neural Networks for Quantum Feedback”, Thomas Fösel, Petru Tighineanu, Talitha Weiss, Florian Marquardt)

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