Quantum sensing and metrology with entangled states

Sijin Li, Wei Wang

Quantum metrology exploits quantum states to achieve an estimation sensitivity beyond classical limits. In the continuous-variable (CV) regime, the squeezed state has been used to implement deterministic quantum sensing, but the quantum metrology sensitivity of this state is significantly affected by losses or detection inefficiencies, which restrict its applications. In this work, quantum distributed sensing is proposed using optical parametric amplified multimode entanglement generated from squeezed states. It is found that the sensitivity is robust to loss or detection inefficiency when large-gain optical parametric amplification (OPA) is introduced, where a two-mode Einstein--Podolsky--Rosen-entangled state and a four-mode cluster state are exploited for analysis. The quantum sensitivity is greatly improved compared to that without OPA in almost all loss scenarios. The quantum Fisher matrix is calculated for both states to obtain the optimal bound in comparison with our scheme, and it is found that even with a small or moderate OPA gain, quantum sensing can be improved compared with the traditional scheme. The states are also compared with corresponding single-mode squeezed states, finding the parameter ranges where entangled states perform better. This study provides a method for realizing large-scale quantum metrology in real-world applications despite losses or detection inefficiencies.

Uman Khalid, Trung Q. Duong, Hyundong Shin

The detection of fundamental quantum resources-namely coherence, discord, and entanglement-benchmarks the metrological power of quantum sensing networks. Traditional methods for certifying these resources, like exhaustive optimization-based tomographic procedures, are resource-intensive and vary significantly. This paper proposes a framework for identifying metrologically useful quantum sensing probes by detecting fundamental quantum resources. Herein, we introduce a witness-based certification method that is experimentally accessible and efficient, though it has limitations in reliability and universality. To address these, we employ artificial neural networks (ANNs) to classify quantum states into resourceful and resourceless states, enhancing the scope and reliability of proposed framework. The performance of ANN-based quantum state classification is also analyzed, positioning ANNs as effective tools for data-driven detection of metrologically resourceful states in quantum sensing tasks.

David S. Simon

Syed Muhammad Abuzar Rizvi, Naema Asif, Muhammad Shohibul Ulum +2 more

Quantum entanglement is a unique phenomenon of quantum mechanics, which has no classical counterpart and gives quantum systems their advantage in computing, communication, sensing, and metrology. In quantum sensing and metrology, utilizing an entangled probe state enhances the achievable precision more than its classical counterpart. Noise in the probe state preparation step can cause the system to output unentangled states, which might not be resourceful. Hence, an effective method for the detection and classification of tripartite entanglement is required at that step. However, current mathematical methods cannot robustly classify multiclass entanglement in tripartite quantum systems, especially in the case of mixed states. In this paper, we explore the utility of artificial neural networks for classifying the entanglement of tripartite quantum states into fully separable, biseparable, and fully entangled states. We employed Bell's inequality for the dataset of tripartite quantum states and train the deep neural network for multiclass classification. This entanglement classification method is computationally efficient due to using a small number of measurements. At the same time, it also maintains generalization by covering a large Hilbert space of tripartite quantum states.