Robustness of quantum-enhanced adaptive phase estimation

Abstract

As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the policies. The robustness test is performed on adaptive phase estimation by simulating the scheme under four phase noise models corresponding to the normal-distribution noise, the random telegraph noise, the skew-normal-distribution noise, and the log-normal-distribution noise. The control policies are devised either by a reinforcement-learning algorithm in the same noise condition, albeit ignorant of its properties, or a Bayesian-based feedback method that assumes no noise. Our robustness test and resource comparison can be used to determining the efficacy and selecting a suitable policy.

Geometric perspective on quantum parameter estimation

“…For a review of quantum feedback control techniques, see Serafini 102 . Palittapongarnpim and Sanders proposed tests to see whether adaptive strategies in quantum metrology are robust against phase noise 103 .…”

Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

“…The problem of quantum phase estimation relies on a sequential and cumulative set of measurements to drive the estimation process, thus making it an ideal problem for reinforcement learning algorithms. In this work, we considered the Differential Evolution (DE) [51,52] and the Particle Swarm Optimization (PSO) [53][54][55], among other reinforcement learning algorithms, as they are the most commonly employed for similar tasks in literature [41][42][43][44][45][46][47]. These algorithms employ a direct search method to the exploration of the search space generated by all the possible policy configurations.…”

“…The incorporation of machine learning techniques in estimation protocols is a natural step forward. Seminal theoretical work on employing reinforcement learning algorithms has demonstrated the potential of these methods for reaching sensitivities below the SQL when used in conjunction with entanglement [41][42][43][44][45][46][47]. Recently, some of these methods have been tested experimentally in an optics setup with their estimation precision being limited by the SQL [48].…”

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

“…These are capable of handling large data sets and of solving tasks for which they have not been explicitly programmed; applications range from stock-price predictions [11,12] to the analysis of medical diseases [13]. In the past few years, several applications of machine-learning methods in the quantum domain have been reported [14][15][16], including state and unitary tomography [17][18][19][20][21][22][23][24][25], the design of quantum experiments [26][27][28][29][30][31][32], the validation of quantum technology [33][34][35], the identification of quantum features [36,37], and the adaptive control of quantum devices [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Also, photonic platforms can be exploited for the realization of machine-learning protocols [55,56]...…”