  The recent advances in machine learning hold great promise for the fields of
quantum sensing and metrology. With the help of reinforcement learning, we can
tame the complexity of quantum systems and solve the problem of optimal
experimental design. Reinforcement learning is a powerful model-free technique
that allows an agent, typically a neural network, to learn the best strategy to
reach a certain goal in a completely a priori unknown environment. However, in
general, we know something about the quantum system with which the agent is
interacting, at least that it follows the rules of quantum mechanics. In
quantum metrology, we typically have a model for the system, and only some
parameters of the evolution or the initial state are unknown. We present here a
general machine learning technique that can optimize the precision of quantum
sensors, exploiting the knowledge we have on the system through model-aware
reinforcement learning. This framework has been implemented in the Python
package qsensoropt, which is able to optimize a broad class of problems found
in quantum metrology and quantum parameter estimation. The agent learns an
optimal adaptive strategy that, based on previous outcomes, decides the next
measurements to perform. We have explored some applications of this technique
to NV centers and photonic circuits. So far, we have been able to certify
better results than the current state-of-the-art controls for many cases. The
machine learning technique developed here can be applied in all scenarios where
the quantum system is well-characterized and relatively simple and small. In
these cases, we can extract every last bit of information from a quantum sensor
by appropriately controlling it with a trained neural network. The qsensoropt
software is available on PyPI and can be installed with pip.
