  Quantum phase estimation is a paradigmatic problem in quantum sensing
andmetrology. Here we show that adaptive methods based on classical
machinelearning algorithms can be used to enhance the precision of quantum
phase estimation when noisy non-entangled qubits are used as sensors. We employ
the Differential Evolution (DE) and Particle Swarm Optimization (PSO)
algorithms to this task and we identify the optimal feedback policies which
minimize the Holevo variance. We benchmark these schemes with respect to
scenarios that include Gaussian and Random Telegraph fluctuations as well as
reduced Ramsey-fringe visibility due to decoherence. We discuss their
robustness against noise in connection with real experimental setups such as
Mach-Zehnder interferometry with optical photons and Ramsey interferometry in
trapped ions,superconducting qubits and nitrogen-vacancy (NV) centers in
diamond.
