Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.
Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.
QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.
Quantum sensors offer unprecedented sensitivity and precision in measurements, but their practical deployment is hampered by challenges in synchronization across networks. This paper investigates a novel protocol, "quantum entanglement synchronization," designed to address this challenge, enabling distributed sensing with enhanced accuracy and resilience to noise. We propose an algorithm that exploits the inherent correlations between entangled particles to maintain the state coherence of quantum sensors, facilitating robust and reliable data acquisition. The core mechanism utilizes a quantum entanglement synchronization algorithm, designed to maintain state fidelity across a network of sensors. The proposed approach has the potential to revolutionize sensor networks, significantly improving data quality and enabling advanced sensing applications. The paper presents a detailed explanation of the algorithm, its implementation, and preliminary results demonstrating its effectiveness.
This paper explores the potential of coordinated optimization within a network of quantum sensors for enhanced sensing capabilities in quantum information physics. The core challenge lies in leveraging the inherent sensitivity of individual quantum sensors while mitigating noise and maximizing overall system performance. We propose a framework utilizing advanced optimization algorithms to achieve this coordination. The framework considers the interconnectedness of sensors, accounting for correlated noise and exploiting synergistic effects. Mathematical models are presented to formalize the optimization problem, including expressions for sensor measurement uncertainties, noise correlation matrices, and the objective functions. We detail a multi-objective optimization approach, balancing precision, sensitivity, and robustness. The theoretical framework lays the groundwork for future experimental implementations, aiming to significantly improve the accuracy and reliability of quantum sensing applications. Key aspects covered include sensor network design, noise characterization, optimization algorithm selection, and performance evaluation metrics.
Quantum metrology takes advantage of nonclassical resources such as squeezing and entanglement to achieve a sensitivity level below the standard quantum limit (SQL). To date, most demonstrations of quantum sensing are restricted to a single sensor, nevertheless, many sensing tasks rely on an array of sensors working collectively. The measurement sensitivity of separable sensors scales as 1/sqrt(M) known as the SQL where M is the number of sensors. Shared entanglement among all sensors can be harnessed to surpass the SQL. Recent theoretical advances in distributed quantum sensing have shown that multipartite entangled states give rise to an improvement of 1/sqrt(M) in measurement sensitivity over separable states when estimating a global parameter. We develop a reconfigurable entangled sensor network based on continuous variable multipartite entangled states. The demonstrated entangled sensor network is composed of three sensor nodes, each equipped with an electro-optic transducer for the detection of radio-frequency (RF) signals. By properly tailoring the entangled states through a variational quantum circuit (VQC), the entangled sensor network can be reconfigured to minimize the measurement noise by more than 3dB below the SQL in different distributed RF sensing tasks, e.g., measuring the angle of arrival and mean amplitude of incident RF waves. Such a capability of capturing global features of interrogating objects by a reconfigurable entangled sensor network with less quantum noise further creates opportunities to enable a quantum advantage in data-processing problems. VQCs in conjunction with classical processing constitutes a promising architecture for quantum simulations, classical optimization, and machine learning. We train the VQC by classical machine learning algorithms to optimize the entanglement shared by the sensors for solving practical data processing problems. We show an entanglement-enabled reduction in error probability for classification of multidimensional RF signals. Our work establishes the quantum advantage of distributed quantum sensing and would lead to applications in ultrasensitive positioning, navigation, and data processing.
Variational Quantum Algorithms are a major method of harnessing Noisy Intermediate-Scale Quantum devices to solve classically intractable problems in physics, chemistry, optimization, and machine learning. The current review offers a critical overview of VQAs, with special emphasis on their theoretical backgrounds, algorithmic structures, and application performance within realistic models of noise. Several key variants, such as the Variational Quantum Eigensolver, Quantum Approximate Optimization Algorithm, and Quantum Neural Networks, are compared in terms of noise robustness, ansatz archi- tecture, and hybrid quantum-classical methods of optimization. Benchmarks in fields such as quantum chemistry, finance modeling, and particles and nuclei physics reveal the poten- tial of VQAs in achieving practical quantum advantage. Fundamentally limiting factors 1such as barren plateaus, optimization overhead, and hardware constraints are considered in addition to mitigation strategies, including adaptive construction of ansatz, informed training in the presence of noise, and quantum error correction using QVECTOR. The paper is concluded with an overview of the scalability and future of VQAs in the direction of transitioning toward fault-tolerant quantum computing.
Quantum sensing networks (QSNs) embody a fusion of quantum sensing and quantum communication, achieving both Heisenberg precision and unconditional security through the exploitation of quantum phenomena such as superposition and entanglement. However, the scalability and sensing capability of QSNs in the noisy intermediate-scale quantum (NISQ) era face challenges due to technological constraints, device imperfections, quantum noise, and dynamic sensing scenarios. Recent advancements in integrating quantum non-terrestrial networks (NTNs) and terrestrial networks (TNs) have paved the way for a global-scale quantum internet. Furthermore, leveraging variational quantum sensing (VQS), which capitalizes on hybrid quantum-classical computing, enhances quantum sensing capabilities. VQS tailors quantum sensing probes for dynamic sensing scenarios and noisy environments, significantly advancing application-specific NISQ sensing. We propose an integrated framework by combining VQS for optimal probe preparation and dynamic utility, as well as satellite-ground quantum networking for establishing on-demand entanglement across remote TN nodes. The integrated framework is advantageous over the standalone ground QSNs in terms of network scalability and sensing capability. Simulation results validate the effectiveness of the proposed framework, thereby introducing prospects for scalable and efficient quantum sensing on a global scale.