Quantum ring all-reduce: communication and privacy advantages for distributed learning
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 pr...
| Authors | María Gragera Garcés, Lirandë Pira |
|---|---|
| Primary category | quant-ph |
| Published | 18 June 2026 |
| Last updated at source | 23 June 2026 |
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