**Quantum phase estimation** is a crucial problem in quantum sensing, where the goal is to enhance the precision of measurements. Adaptive methods are employed to achieve this goal, and benchmarks are used to evaluate the effectiveness of these methods. #### Benchmarks - The benchmarks for quantum phase estimation include algorithms such as **Differential Evolution (DE) and Particle Swarm Optimization (PSO).** - These algorithms are used to **identify optimal feedback policies** that can minimize the Holevo variance, which is a measure of the [[uncertainty]] in the estimated phase. #### Noise Scenarios - The benchmarks are also **tested against various noise scenarios**, including Gaussian and Random Telegraph fluctuations, which are common sources of noise in quantum systems. - Reduced Ramsey-fringe visibility due to decoherence is another noise scenario that is considered in these benchmarks. #### Experimental Setups - The robustness of the adaptive methods against these noise scenarios is discussed in connection with real experimental setups such as Mach–Zehnder interferometry with optical photons, Ramsey interferometry in trapped ions, superconducting qubits, and nitrogen-vacancy (NV) centers in diamond. - These experimental setups provide a realistic testbed for evaluating the performance of the adaptive methods in the presence of noise.