Physics-Informed Quantum Communication Networks: A Vision Towards the Quantum Internet
Not on the map. Why?
Abstract (arXiv)
Quantum communications is a promising technology that will play a fundamental role in the design of future networks. In fact, significant efforts are being dedicated by both the quantum physics and the classical communications communities on developing new architectures, solutions, and practical implementations of quantum communication networks (QCNs). Although these efforts led to various advances in today's technologies, there still exists a non-trivial gap between the research efforts of the two communities on designing and optimizing the performance of QCNs. For instance, most prior works by the classical communications community ignore important quantum physics-based constraints when designing QCNs. For example, many existing works on entanglement distribution do not account for the decoherence of qubits inside quantum memories and, thus, their designs become impractical since they assume an infinite lifetime of quantum states. In this paper, we bring forth a novel analysis of the performance of QCNs in a physics-informed manner, by relying on the quantum physics principles that underly the different components of QCNs. The need of the physics-informed approach is then assessed and its fundamental role in designing practical QCNs is analyzed across various open research areas. Moreover, we identify novel physics-informed performance metrics and controls that enable QCNs to leverage the state-of-the-art advancements in quantum technologies to enhance their performance. Finally, we analyze multiple pressing challenges and open research directions in QCNs that must be treated using a physics-informed approach to lead practically viable results. Ultimately, this work attempts to bridge the gap between the classical communications and the quantum physics communities in the area of QCNs to foster the development of the future communication networks towards the quantum Internet.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
- 2023Auxiliary Task-based Deep Reinforcement Learning for Quantum Controlcited by 2
- 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networkscited by 1
- 2026SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networkscited by 0
- 2026SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks10 Apr 2026
- 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks27 Nov 2025
- 2023Federated Quantum Long Short-term Memory (FedQLSTM)21 Dec 2023
- 2023Auxiliary Task-based Deep Reinforcement Learning for Quantum Control28 Feb 2023