# Quantum ring all-reduce: communication and privacy advantages for distributed learning

arXiv: https://arxiv.org/abs/2606.20344 · HTML: https://mlsanity.com/paper/2606.20344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [Privacy-preserving quantum federated learning via gradient hiding](https://mlsanity.com/paper/2312.04447/) (2023)
- [An Invitation to Distributed Quantum Neural Networks](https://mlsanity.com/paper/2211.07056/) (2022)
- [Blind quantum machine learning with quantum bipartite correlator](https://mlsanity.com/paper/2310.12893/) (2023)
- [Exponential Quantum Communication Advantage in Distributed Inference and Learning](https://mlsanity.com/paper/2310.07136/) (2023)
- [CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2602.22269/) (2026)
- [Distributed Quantum Property Testing with Communication Constraints](https://mlsanity.com/paper/2604.05962/) (2026)

## Abstract (arXiv)

Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this work, we explore how quantum communications can make distributed training both more communication-efficient and information-theoretically private, for both classical and quantum learning models. Ring all-reduce is the foundational communication primitive for large-scale distributed training. We present a quantum version that reduces per-link online communication by a provably optimal factor of two using pre-shared entanglement and superdense coding, without requiring the learning model or gradient computation to change. Beyond bandwidth, the primitive enables privacy guarantees that are information-theoretically impossible for any classical protocol, achieving composable ε-secure aggregation, via verified entanglement, at a 2x overhead in GHZ copies. Our hybrid quantum-classical communication architecture yields simultaneous communication and security advantages for large scale distributed training, regardless of whether the learning itself is quantum or classical. Finally, we characterise quantum advantages in gradient conflict detection for server-to-client communication under bandwidth constraints, a setting that arises after ring all-reduce is completed, when full gradient broadcast to external clients is infeasible. Two variants of the problem admit different separations. For margin-based alignment testing (textscGapIPτ), the quantum advantage is quadratic in the margin parameter: widetildeO(τ-1log P) qubits versus widetildeO(min(τ-2,P)) bits. For sign-consistency auditing against a private parameter matching (textscTieAuditε), the advantage represents an exponential separation in communication complexity: Omega(√P) bits whereas O(ε-2log P) qubits suffice.