quantum-ring-allreduce-distributed

name	quantum-ring-allreduce-distributed
description	Quantum ring all-reduce protocol for distributed ML training — uses pre-shared entanglement and superdense coding for 2x communication reduction plus information-theoretic privacy guarantees. Covers composable ε-secure aggregation via verified entanglement and exponential separation in sign-consistency auditing.
category	quantum
trigger_words	quantum ring all-reduce, distributed quantum training, superdense coding, quantum communication complexity, secure aggregation, gradient conflict detection, quantum distributed learning, entanglement distribution, GHZ aggregation
arxiv_id	2606.20344
created	2026-07-05T00:00:00.000Z

Quantum Ring All-Reduce for Distributed Learning

Core Methodology

1. Quantum Ring All-Reduce with Superdense Coding

• Uses pre-shared entanglement (Bell pairs / GHZ states) between ring participants
• Superdense coding halves per-link online communication: 2 classical bits per 1 qubit
• Learning model and gradient computation remain unchanged — pure communication primitive
• Works for both classical and quantum learning models

2. Composable ε-Secure Aggregation

• Privacy guarantees information-theoretically impossible for any classical protocol
• Achieved via verified entanglement at 2x overhead in GHZ copies
• Hybrid quantum-classical communication architecture

3. Gradient Conflict Detection (Two Variants)

• Margin-based alignment testing (GapIP_τ): Quadratic quantum advantage
  • Quantum: Õ(τ⁻¹ log P) qubits
  • Classical: Õ(min(τ⁻², P)) bits
• Sign-consistency auditing (TieAudit_ε): Exponential separation
  • Quantum: O(ε⁻² log P) qubits
  • Classical: Ω(√P) bits

Implementation Steps

1. Preparation phase: Distribute Bell pairs / GHZ states across ring topology
2. Gradient encoding: Each worker encodes gradients via superdense coding
3. Ring reduction: Sequential accumulation with quantum communication
4. Verification: Entanglement verification for security guarantees
5. Conflict detection: Quantum protocols for gradient auditing under bandwidth constraints

Key Trade-offs

Metric	Classical	Quantum	Advantage
Per-link communication	2x	1x (superdense)	2x reduction
Privacy	Computational	Information-theoretic	Fundamental
Sign-audit complexity	Ω(√P) bits	O(log P) qubits	Exponential

Pitfalls

• Requires quantum communication infrastructure between training nodes
• Entanglement distribution and maintenance overhead
• GHZ state preparation fidelity requirements
• Not yet practical for current NISQ-era networks (research-level protocol)