Quantum Hamlets: Distributed Compilation of Large Algorithmic Graph States

TL;DR

This paper presents a scalable partitioning algorithm, BURY, that reduces entanglement and Bell pair requirements for distributed quantum graph state generation, improving efficiency in quantum networks.

Contribution

Introduces BURY, a heuristic graph partitioning algorithm that minimizes Bell pairs and cut-rank, enhancing distributed quantum graph state compilation.

Findings

BURY reduces Bell pair requirements compared to state-of-the-art algorithms.

Partitioning with BURY lowers the cut-rank, indicating less entanglement needed.

Applicable to dynamic and measurement-based quantum computation scenarios.

Abstract

We investigate the problem of compiling the generation of graph states to arbitrarily many distributed homogeneous quantum processing units (QPUs), providing a scalable partitioning algorithm and graph state generation protocol to minimize the number of Bell pairs required. Current approaches focus on the naive metric of cut edges to estimate the quantum communication cost. We show that the problem of balanced k graph partitioning, with the objective of minimizing the sizes of the maximum matchings between the partitions, leads to lower entanglement requirements across partitions. Our heuristic algorithm, BURY, partitions graph states to require fewer Bell pairs for generation than state-of-the-art k partition algorithms. Furthermore, we show that BURY reduces the cut-rank of the partitions, demonstrating that the partitioning found by our algorithm is likely to minimize the Bell pair utilization of any future improved distributed graph state generation protocol. We also discuss how our methods apply to the dynamic case where the graph state generation and measurement are performed concurrently. Our compilation approach provides a scalable foundation for reducing quantum network overhead for distributed measurement-based quantum computation (MBQC), as well as any scheme where distributed graph state generation is desired.