Towards Distributed Quantum Computing by Qubit and Gate Graph Partitioning Techniques

TL;DR

This paper proposes graph-based partitioning techniques for distributed quantum computing, balancing entanglement overhead and runtime to optimize execution across small quantum computers.

Contribution

It introduces two novel partitioning methods considering gate and state teleportation, applying METIS for optimized quantum circuit distribution.

Findings

Partitioning effectiveness varies with circuit type

Gate and state teleportation approaches impact entanglement overhead

Simulation results guide optimal partitioning strategies

Abstract

Distributed quantum computing is motivated by the difficulty in building large-scale, individual quantum computers. To solve that problem, a large quantum circuit is partitioned and distributed to small quantum computers for execution. Partitions running on different quantum computers share quantum information using entangled Bell pairs. However, entanglement generation and purification introduces both a runtime and memory overhead on distributed quantum computing. In this paper we study that trade-off by proposing two techniques for partitioning large quantum circuits and for distribution to small quantum computers. Our techniques map a quantum circuit to a graph representation. We study two approaches: one that considers only gate teleportation, and another that considers both gate and state teleportation to achieve the distributed execution. Then we apply the METIS graph partitioning algorithm to obtain the partitions and the number of entanglement requests between them. We use the SeQUeNCe quantum communication simulator to measure the time required for generating all the entanglements required to execute the distributed circuit. We find that the best partitioning technique will depend on the specific circuit of interest.