Improving the dynamics of quantum sensors with reinforcement learning

Abstract

Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross-entropy method of reinforcement learning (RL) to optimize the strength and position of control pulses. Compared to the quantumchaotic sensors with periodic control pulses in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the RL method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.

Data-Centric Machine Learning in Quantum Information Science

“…Machine learning (ML) is quickly becoming a standard tool for approaching and analyzing problems in quantum information science (QIS). Recent applications include state classification [1][2][3][4], quantum control [5][6][7][8][9], sensing [10][11][12], parameter estimation for deployed systems [13,14], turbulence correction [15][16][17][18][19], and state reconstruction [20], among many others [21][22][23][24][25]. Although the motivations for adopting ML in the QIS context vary, they are often related to the ability of ML systems to perform optimization tasks in highly constrained or nonconvex situations and the potential improvements in resource scaling compared to standard techniques.…”

Quantum metrology with a non-linear kicked Mach-Zehnder interferometer

“…This is a relevant description of atomic-vapor magnetometers, and recently it was realized that the sensitivity of the device, based on the precession of the collective spin of the ensemble in a magnetic field, can be substantially increased by "kicking" it periodically with laser pulses that induce non-linear rotations and drive the sensor into a quantum-chaotic regime [16]. Moreover, these kicks introduce new degrees of freedom that can be optimized and adapted via machine learning to the dissipative environment, leading to a robust way of fighting decoherence and enhancing the sensitivity beyond what is classically possible [17]. It is therefore natural to ask, whether something similar can be achieved with a Mach-Zehnder interferometer.…”

Deep reinforcement learning for efficient measurement of quantum devices

“…Neural network-based deep reinforcement learning (DRL) algorithms have proven to be very successful by surpassing human experts in domains such as the popular Atari 2600 games 1 , chess 2 , and Go 3 . RL algorithms are expected to advance the control of quantum devices [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] , because the models can be robust against noise and stochastic elements present in many physical systems and they can be trained without labelled data. However, the potential of deep reinforcement learning for the efficient measurement of quantum devices is still unexplored.…”