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Communications in computer and information science

Energy Optimization in Linear Sensor Networks via Quantum Entanglement: A Theoretical and Simulative Study

Carlos Antonio Ayala Tlalolini, Yunia Verónica García‐Tejeda, Victor Barrera-Figueroa, Aldo Hernández-Díaz

Published Oct 31, 2025• 1 citation
Publisher
DOI
10.1007/978-3-032-09738-5_4

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Quantum-Guided Wave Propagation for Sensor Networks Quantum entanglement offers a unique approach to enhancing wireless communication and sensor network reliability, particularly concerning the propagation of waves. This paper investigates a novel method for guiding wave propagation through sensor networks utilizing quantum entanglement, aiming to optimize signal strength, reduce noise, and ultimately improve data collection accuracy. The core mechanism involves implementing a "Quantum-Guided Propagation Algorithm" – an algorithm that leverages quantum entanglement to dynamically adjust wave propagation paths, creating a more efficient and robust communication pathway. This work addresses a critical challenge in sensor networks, focusing on mitigating signal degradation caused by interference and noise. The proposed algorithm demonstrates potential improvements in data collection reliability and overall network performance through a rigorous theoretical analysis and experimental validation. We present a detailed explanation of the algorithm's core components, its optimization process, and the results achieved through simulation and preliminary testing. The benefits of this approach are highlighted, offering a significant advancement in the field of quantum-enhanced wireless communication.
In recent years, distributed quantum sensing has gained interest for a range of applications requiring networks of sensors, from global-scale clock synchronization to high energy physics. In particular, a network of entangled sensors can improve not only the sensitivity beyond the shot noise limit, but also enable a Heisenberg scaling with the number of sensors. Here, using bright entangled twin beams, we theoretically and experimentally demonstrate the detection of a linear combination of two distributed phases beyond the shot noise limit with a truncated SU(1,1) interferometer. Specifically, we show a quantum noise reduction of 1.7 ± 0.3 dB below what is possible with the corresponding classical configuration. Additionally, we theoretically extend the use of a truncated SU(1,1) interferometer to a multi-phase-distributed sensing scheme that leverages entanglement as a resource to achieve a quantum improvement in the scaling with the number of sensors in the network. Our results pave the way for developing quantum-enhanced sensor networks that can achieve an entanglement-enhanced sensitivity.
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.