Time-local optimal control for parameter estimation in the Gaussian regime

Abstract

Information about a classical parameter encoded in a quantum state can only decrease if the state undergoes a non-unitary evolution, arising from the interaction with an environment. However, instantaneous control unitaries may be used to mitigate the decrease of information caused by an open dynamics. A possible, locally optimal (in time) choice for such controls is the one that maximises the time-derivative of the quantum Fisher information (QFI) associated with a parameter encoded in an initial state. In this study, we focus on a single bosonic mode subject to a Markovian, thermal master equation, and determine analytically the optimal time-local control of the QFI for its initial squeezing angle (optical phase) and strength. We show that a single initial control operation is already optimal for such cases and quantitatively investigate situations where the optimal control is applied after the open dynamical evolution has begun.

Optimally controlled quantum discrimination and estimation

“…Quantum discrimination and quantum estimation underlie many applications in quantum information science, including quantum hypothesis testing, quantum detection, and quantum sensing. While quantum control has been employed to improve the precision in quantum estimation [47][48][49][50][51][52][53][54][55][56], the use of quantum control in quantum discrimination remains scarce [57][58][59]. This is so despite the fact that one may expect quantum control to help identify fundamental performance bounds of quantum discrimination, similar to those found for quantum computation [6,60] or derive pulse shapes for improved performance with direct relevance to experiments [8,61].…”

Efficient and robust entanglement generation with deep reinforcement learning for quantum metrology

“…It can provide a machine learning (ML) model, often neural networks that is capable of optimizing a certain objective function by providing a well-designed time sequence of control procedures [41,42]. It is particularly suitable for seeking the optimal preparation of desired quantum states [43][44][45][46][47][48][49][50][51]. Recently, it is proposed that extreme spin squeezing can be achieved with OAT interaction using a sequence of rotation pulses designed via DRL [44].…”

Efficient and Robust Entanglement Generation with Deep Reinforcement Learning for Quantum Metrology

“…It can provide a machine learning (ML) model, often neural networks that is capable of optimizing a certain objective function by providing a well-designed time sequence of control procedures. It is particularly suitable for seeking the optimal preparation of desired quantum states [41][42][43][44][45][46][47][48][49]. Recently, it is proposed that extreme spin squeezing can be achieved with OAT interaction using a sequence of rotation pulses designed via DRL [42].…”