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Quantum Communication Networks Enhanced by Distributed Quantum Memories

Xiangyi Meng, Nicolò Lo Piparo, Kae Nemoto, and István A. Kovács

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Researchers from Eötvös Loránd University and Bar-Ilan University demonstrated that distributed quantum memories in communication networks create a fundamental advantage through network-wide synergy, surpassing previous point-to-point limitations. The team introduced α-percolation, a novel statistical physics model combining quantum distillation and relay protocols, enabling remote access to memories and improving communication efficiency in complex topologies. Graph-merging rules—analogous to renormalization group methods—allow flexible, order-independent optimization, producing a "positive feedback" mechanism that enhances entanglement distribution across disconnected network components. Quantum memories act as "cloud storage" for flying qubits, enabling spatially separated nodes to share entangled states via remote teleportation, reducing latency and improving scalability in large-scale networks. This work suggests a paradigm shift in quantum network architecture, emphasizing collective memory usage and emergent statistical behaviors for future quantum internet designs.

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AbstractBuilding large-scale quantum communication networks has its unique challenges. Here, we demonstrate that a network-wide synergistic usage of quantum memories distributed in a quantum communication network offers a fundamental advantage. We first map the problem of quantum communication with local usage of memories into a classical continuum percolation model. Then, we show that this mapping can be improved through a cooperation of quantum distillation and relay protocols via remote access to distributed memories. This improved mapping, which we term $\alpha$-percolation, can be formulated in terms of graph-merging rules, analogous to the decimation rules of the renormalization group treatment of disordered quantum magnets. These rules can be performed in any order, yielding the same optimal result that is characterized by the emergence of a “positive feedback'' mechanism and the formation of spatially disconnected “hopping'' communication components – both marking significant improvements beyond the traditional point-to-point consideration of quantum communication in networked structures.Featured image: Left: Quantum memories distributed across two connected components (the X- and Y-shaped) effectively act as “cloud storage” for remotely storing flying qubits generated between the nearest nodes of the two components. Right: A connected component can also act as a relay to swap flying qubits between its neighbors.Popular summaryIt is well established that efficient quantum communication relies on quantum memories. Here, however, we show that the collective advantage of using memories is far more profound than previously anticipated, particularly within a complex network topology. By leveraging network-wide synergy—specifically through remote distillation (remote gate teleportation) and relay protocols between interconnected nodes—communication efficiency is significantly enhanced. This results in a novel statistical physics model for large-scale quantum networks—which we term α-percolation—that reveals unique emergent statistical behaviors and calls for a distinct architectural design for such large-scale systems.► BibTeX data@article{Meng2025quantum, doi = {10.22331/q-2025-12-15-1948}, url = {https://doi.org/10.22331/q-2025-12-15-1948}, title = {Quantum {C}ommunication {N}etworks {E}nhanced by {D}istributed {Q}uantum {M}emories}, author = {Meng, Xiangyi and Lo Piparo, Nicol{\`{o}} and Nemoto, Kae and Kov{\'{a}}cs, Istv{\'{a}}n A.}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {9}, pages = {1948}, month = dec, year = {2025} }► References [1] M. Razavi, M. Piani, and N. Lütkenhaus. ``Quantum repeaters with imperfect memories: Cost and scalability''. Phys. Rev. A 80, 032301 (2009). https://doi.org/10.1103/PhysRevA.80.032301 [2] Mikael Afzelius, Nicolas Gisin, and Hugues de Riedmatten. ``Quantum memory for photons''. Phys. Today 68, 42–47 (2015). https://doi.org/10.1063/PT.3.3021 [3] Yumang Jing and Mohsen Razavi. ``Quantum Repeaters with Encoding on Nitrogen-Vacancy-Center Platforms''. Phys. Rev. Appl. 18, 024041 (2022). https://doi.org/10.1103/PhysRevApplied.18.024041 [4] Ofir Milul, Barkay Guttel, Uri Goldblatt, Sergey Hazanov, Lalit M. Joshi, Daniel Chausovsky, Nitzan Kahn, Engin Çiftyürek, Fabien Lafont, and Serge Rosenblum. ``Superconducting Cavity Qubit with Tens of Milliseconds Single-Photon Coherence Time''. PRX Quantum 4, 030336 (2023). https://doi.org/10.1103/PRXQuantum.4.030336 [5] Sonali Gera, Chase Wallace, Mael Flament, Alessia Scriminich, Mehdi Namazi, Youngshin Kim, Steven Sagona-Stophel, Giuseppe Vallone, Paolo Villoresi, and Eden Figueroa. ``Hong-Ou-Mandel interference of single-photon-level pulses stored in independent room-temperature quantum memories''. npj Quantum Inf. 10, 1–8 (2024). https://doi.org/10.1038/s41534-024-00803-2 [6] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. ``Quantum machine learning''. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474 [7] Hsin-Yuan Huang, Richard Kueng, and John Preskill. ``Information-Theoretic Bounds on Quantum Advantage in Machine Learning''.

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Could not fetch ADS cited-by data during last attempt 2025-12-28 12:45:07: No response from ADS or unable to decode the received json data when getting the list of citing works.This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions. AbstractBuilding large-scale quantum communication networks has its unique challenges. Here, we demonstrate that a network-wide synergistic usage of quantum memories distributed in a quantum communication network offers a fundamental advantage. We first map the problem of quantum communication with local usage of memories into a classical continuum percolation model. Then, we show that this mapping can be improved through a cooperation of quantum distillation and relay protocols via remote access to distributed memories. This improved mapping, which we term $\alpha$-percolation, can be formulated in terms of graph-merging rules, analogous to the decimation rules of the renormalization group treatment of disordered quantum magnets. These rules can be performed in any order, yielding the same optimal result that is characterized by the emergence of a “positive feedback'' mechanism and the formation of spatially disconnected “hopping'' communication components – both marking significant improvements beyond the traditional point-to-point consideration of quantum communication in networked structures.Featured image: Left: Quantum memories distributed across two connected components (the X- and Y-shaped) effectively act as “cloud storage” for remotely storing flying qubits generated between the nearest nodes of the two components. Right: A connected component can also act as a relay to swap flying qubits between its neighbors.Popular summaryIt is well established that efficient quantum communication relies on quantum memories. Here, however, we show that the collective advantage of using memories is far more profound than previously anticipated, particularly within a complex network topology. By leveraging network-wide synergy—specifically through remote distillation (remote gate teleportation) and relay protocols between interconnected nodes—communication efficiency is significantly enhanced. This results in a novel statistical physics model for large-scale quantum networks—which we term α-percolation—that reveals unique emergent statistical behaviors and calls for a distinct architectural design for such large-scale systems.► BibTeX data@article{Meng2025quantum, doi = {10.22331/q-2025-12-15-1948}, url = {https://doi.org/10.22331/q-2025-12-15-1948}, title = {Quantum {C}ommunication {N}etworks {E}nhanced by {D}istributed {Q}uantum {M}emories}, author = {Meng, Xiangyi and Lo Piparo, Nicol{\`{o}} and Nemoto, Kae and Kov{\'{a}}cs, Istv{\'{a}}n A.}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {9}, pages = {1948}, month = dec, year = {2025} }► References [1] M. Razavi, M. Piani, and N. Lütkenhaus. ``Quantum repeaters with imperfect memories: Cost and scalability''. Phys. Rev. A 80, 032301 (2009). https://doi.org/10.1103/PhysRevA.80.032301 [2] Mikael Afzelius, Nicolas Gisin, and Hugues de Riedmatten. ``Quantum memory for photons''. Phys. Today 68, 42–47 (2015). https://doi.org/10.1063/PT.3.3021 [3] Yumang Jing and Mohsen Razavi. ``Quantum Repeaters with Encoding on Nitrogen-Vacancy-Center Platforms''. Phys. Rev. Appl. 18, 024041 (2022). https://doi.org/10.1103/PhysRevApplied.18.024041 [4] Ofir Milul, Barkay Guttel, Uri Goldblatt, Sergey Hazanov, Lalit M. Joshi, Daniel Chausovsky, Nitzan Kahn, Engin Çiftyürek, Fabien Lafont, and Serge Rosenblum. ``Superconducting Cavity Qubit with Tens of Milliseconds Single-Photon Coherence Time''. PRX Quantum 4, 030336 (2023). https://doi.org/10.1103/PRXQuantum.4.030336 [5] Sonali Gera, Chase Wallace, Mael Flament, Alessia Scriminich, Mehdi Namazi, Youngshin Kim, Steven Sagona-Stophel, Giuseppe Vallone, Paolo Villoresi, and Eden Figueroa. ``Hong-Ou-Mandel interference of single-photon-level pulses stored in independent room-temperature quantum memories''. npj Quantum Inf. 10, 1–8 (2024). https://doi.org/10.1038/s41534-024-00803-2 [6] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. ``Quantum machine learning''. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474 [7] Hsin-Yuan Huang, Richard Kueng, and John Preskill. ``Information-Theoretic Bounds on Quantum Advantage in Machine Learning''.

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Quantum Communication Networks Enhanced by Distributed Quantum Memories

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