Variational Quantum Integrated Sensing and Communication

2025-11-20

AI Summary

To address the challenge of integrating communication and sensing functionalities in next-generation networks, this paper proposes a quantum entanglement–based integrated sensing-and-communication (ISAC) protocol. Methodologically, it employs a qudit-based variational quantum circuit to jointly optimize quantum encoding, measurement, and classical decoding: quantum entanglement enables simultaneous ultra-dense coding and parameter sensing, while classical machine learning facilitates efficient decoding and high-precision parameter estimation. The key contribution is the first unified framework enabling tunable trade-offs between communication rate and sensing accuracy, explicitly revealing and harnessing the synergistic gain of entanglement resources. Experimental evaluation demonstrates that the protocol achieves significantly improved phase estimation accuracy—while maintaining high classical communication rates—thereby exhibiting superior performance trade-off capability and system scalability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Developing quantum integrated sensing and communication using entanglement
• Optimizing encoding and measurement via variational circuit learning
• Achieving flexible trade-off between communication rate and estimation accuracy

Innovation

• Variational quantum circuits optimize encoding and measurement
• Entanglement enables superdense coding and quantum sensing
• Classical machine learning decoders process measurement outcomes