  It is challenging to construct metrology schemes which harness quantum
features such as entanglement and coherence to surpass the standard quantum
limit. We propose an ansatz for devising adaptive-feedback quantum metrology
(AFQM) strategy which reduces greatly the searching space. Combined with the
Markovian feedback assumption, the computational complexity for designing AFQM
would decrease from $N^7$ to $N^4$ , for N probing systems. The feedback scheme
devising via machine learning such as particle-swarm optimization and
derivative evolution would thus requires much less time and produces equally
well imprecision scaling. We have thus devised an AFQM for 207-partite system.
The imprecision scaling would persist steadily for N > 207 when the parameter
settings for 207-partite system is employed without further training. Our
ansatz indicates an built-in resilience of the feedback strategy against qubit
loss. The feedback strategies designed for the noiseless scenarios have been
tested against the qubit loss noise and the phase fluctuation noise. Our
numerical result confirms great resilience of the feedback strategies against
the two kinds of noise.
