Efficient and Robust Entanglement Generation with Deep Reinforcement
Learning for Quantum Metrology
- URL: http://arxiv.org/abs/2203.00189v1
- Date: Tue, 1 Mar 2022 02:42:41 GMT
- Title: Efficient and Robust Entanglement Generation with Deep Reinforcement
Learning for Quantum Metrology
- Authors: Yuxiang Qiu, Min Zhuang, Jiahao Huang, Chaohong Lee
- Abstract summary: One crucial issue is how to prepare a quantum entangled state suitable for high-precision measurement beyond the standard quantum limit.
Here, we propose a scheme to find optimal pulse sequence to accelerate the one-axis twisting dynamics for entanglement generation with the aid of deep reinforcement learning (DRL)
Our protocol with DRL is efficient and easy to be implemented in state-of-the-art experiments.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum metrology exploits quantum resources and strategies to improve
measurement precision of unknown parameters. One crucial issue is how to
prepare a quantum entangled state suitable for high-precision measurement
beyond the standard quantum limit. Here, we propose a scheme to find optimal
pulse sequence to accelerate the one-axis twisting dynamics for entanglement
generation with the aid of deep reinforcement learning (DRL). We consider the
pulse train as a sequence of $\pi/2$ pulses along one axis or two orthogonal
axes, and the operation is determined by maximizing the quantum Fisher
information using DRL. Within a limited evolution time, the ultimate precision
bounds of the prepared entangled states follow the Heisenberg-limited scalings.
These states can also be used as the input states for Ramsey interferometry and
the final measurement precisions still follow the Heisenberg-limited scalings.
While the pulse train along only one axis is more simple and efficient, the
scheme using pulse sequence along two orthogonal axes show better robustness
against atom number deviation. Our protocol with DRL is efficient and easy to
be implemented in state-of-the-art experiments.
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