The integration of sensing and communication functionalities within a common system is one of the main innovation drivers for next-generation networks. In this paper, we introduce a quantum integrated sensing and communication (QISAC) protocol that leverages entanglement in quantum carriers of information to enable both superdense coding and quantum sensing. The proposed approach adaptively optimizes encoding and quantum measurement via variational circuit learning, while employing classical machine learning-based decoders and estimators to process the measurement outcomes. Numerical results for qudit systems demonstrate that the proposed QISAC protocol can achieve a flexible trade-off between classical communication rate and accuracy of parameter estimation.
The integration of sensing and communication functionalities within a common system is one of the main innovation drivers for next-generation networks. In this paper, we introduce a quantum integrated sensing and communication (QISAC) protocol that leverages entanglement in quantum carriers of information to enable both superdense coding and quantum sensing. The proposed approach adaptively optimizes encoding and quantum measurement via variational circuit learning, while employing classical machine learning-based decoders and estimators to process the measurement outcomes. Numerical results for qudit systems demonstrate that the proposed QISAC protocol can achieve a flexible trade-off between classical communication rate and accuracy of parameter estimation.
Quantum communication and quantum metrology are widely compelling applications in the field of quantum information science, and quantum remote sensing is an intersection of both. Despite their differences, there are notable commonalities between quantum communication and quantum remote sensing, as they achieve their functionalities through the transmission of quantum states. Here we propose a quantum integrated sensing and communication (QISAC) protocol, which achieves quantum sensing under the Heisenberg limit while simultaneously enabling quantum secure communication through the transmission of entanglements. We have theoretically proven its security against eavesdroppers. The security of QISAC is characterized by the secrecy capacity for the information bit as well as asymmetric Fisher information gain for sensing. Through simulations conducted under the constraints of limited entanglement resources, we illustrate that QISAC maintains high accuracy in the estimation of phase. Hence, our QISAC offers a fresh perspective for applications of future quantum networks. Published by the American Physical Society 2024
This paper presents a novel approach to quantum information coding and decoding leveraging the unique properties of quantum entanglement. The core idea is to develop a self-adaptive system capable of dynamically adjusting encoding and decoding parameters based on the observed noise levels and channel losses within the quantum channel. This adaptive strategy aims to significantly improve the efficiency and reliability of quantum information transmission. The proposed framework utilizes a quantum decoherence model to accurately represent channel losses and employs an optimization algorithm rooted in quantum control theory to optimize the generation and measurement strategies of entangled states in real-time. The algorithm's objective is to minimize information loss. The key innovation lies in the system's ability to respond in real-time to fluctuating channel conditions, moving beyond the limitations of static encoding/decoding schemes commonly employed in existing quantum communication protocols. Mathematical models and formulations are presented to illustrate the design and optimization process, focusing on key performance metrics such as channel capacity and fidelity. The system's potential impact on future quantum communication networks is discussed.
Quantum integrated sensing and communication (QISAC) is emerging as a key paradigm for next-generation quantum technologies, in which entanglement, coherence, and non-classical correlations are jointly exploited to enable secure communication, distributed sensing, and networked quantum functionalities. As quantum systems transition from laboratory demonstrations to deployable infrastructures, performance is increasingly limited not by individual components but by cross-layer interactions among hardware noise, channel loss, error-management strategies, and network architecture. This survey presents a system-level architectural framework for QISAC, focusing on how physical-layer constraints, quantum channel characteristics, and error-mitigation mechanisms jointly determine end-to-end performance and architectural feasibility in scalable quantum networks. We analyze representative quantum hardware platforms-including superconducting circuits, trapped ions, neutral atoms, photonic systems, and solid-state spin qubits-through the lens of coherence time, fidelity, interface capability, and technology readiness, emphasizing their implications for networked sensing and communication rather than device-level operation. The roles of quantum error correction (QEC) and entanglement purification are examined as complementary error-management layers, highlighting trade-offs among resource overhead, latency, and achievable entanglement fidelity under realistic noise and memory constraints. We further connect these architectural considerations to QISAC-enabled applications such as distributed quantum sensing, quantum key distribution (QKD), and repeater-assisted entanglement distribution, clarifying how hardware limitations and error-management choices propagate to application-level performance. By synthesizing noise, error management, and network co-design into a unified system perspective, this survey provides design-oriented insights and architectural guidelines for building scalable, noise-resilient QISAC networks.
Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics.Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes.
This paper investigates the potential of utilizing quantum entanglement for adaptive machine learning, specifically focusing on information encoding. The core claim is that leveraging entanglement's unique properties can lead to significantly improved efficiency and accuracy in machine learning models, particularly when dealing with high-dimensional data and complex relationships. The proposed mechanism involves constructing a system capable of real-time measurement and manipulation of entangled states, encoding input data into these states, and employing quantum algorithms, such as Variational Quantum Feature Mapping (VQFM), for learning. Furthermore, an adaptive approach is integrated to optimize the encoding process and feature extraction, capitalizing on the inherent advantages of entanglement. The research addresses a novel area by directly applying entanglement in machine learning rather than solely treating it as a foundational resource for quantum computing, and introduces an adaptive learning framework for enhanced performance. The results presented suggest a promising trajectory for combining quantum mechanics and machine learning to tackle challenging computational problems.
Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration, and gravitational gradients well beyond the limits of classical sensors. However, identifying suitable sensing probes and measurement schemes can be a classically intractable task, as it requires optimizing over Hilbert spaces of high dimension. In variational quantum sensing, a probe quantum system is generated via a parameterized quantum circuit (PQC), exposed to an unknown physical parameter through a quantum channel, and measured to collect classical data. PQCs and measurements are typically optimized using offline strategies based on frequentist learning criteria. This paper introduces an adaptive protocol that uses Bayesian inference to optimize the sensing policy via the maximization of the active information gain. The proposed variational methodology is tailored for non-asymptotic regimes where a single probe can be deployed in each time step, and is extended to support the fusion of estimates from multiple quantum sensing agents.