Pre-Distribution of Entanglements in Quantum Networks

TL;DR

This paper proposes a pre-distribution strategy for entangled pairs in quantum networks to significantly reduce latency, formulating an optimization problem and developing algorithms validated through extensive simulations.

Contribution

It introduces a novel pre-distribution technique for entanglement in quantum networks and formulates an optimization problem with efficient algorithms to minimize latency.

Findings

Pre-distribution reduces entanglement generation latency by up to an order of magnitude.

The proposed algorithms outperform naive approaches in simulation evaluations.

Effective node-pair selection is crucial for optimizing pre-distribution benefits.

Abstract

Quantum network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only viable approach is teleportation of quantum states, which requires a prior distribution of entangled pairs (EPs) of qubits. Establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. To reduce EP generation latency, prior works have looked at selection of efficient entanglement-routing paths and simultaneous use of multiple such paths for EP generation. In this paper, we propose and investigate a complementary technique to reduce EP generation latency--to pre-distribute EPs over certain (pre-determined) pairs of network nodes; these pre-distributed EPs can then be used to generate EPs for the requested pairs, when needed, with lower generation latency. For such an pre-distribution approach to be most effective, we need to address an optimization problem of selection of node-pairs where the EPs should be pre-distributed to minimize the generation latency of expected EP requests, under a given cost constraint. In this paper, we appropriately formulate the above optimization problem and design two efficient algorithms, one of which is a greedy approach based on an approximation algorithm for a special case. Via extensive evaluations over the NetSquid simulator, we demonstrate the effectiveness of our approach and developed techniques; we show that our developed algorithms outperform a naive approach by up to an order of magnitude.