Bipartitioning of Graph States for Distributed Measurement-Based Quantum Computing

TL;DR

This paper presents a simulated annealing algorithm to optimize qubit partitioning in distributed measurement-based quantum computing, reducing inter-node entanglement and improving resource efficiency.

Contribution

It introduces an efficient method for bipartitioning graph states using simulated annealing, specifically minimizing cut rank for distributed MBQC.

Findings

Effective qubit assignment for grid graphs

Reduced inter-node entanglement in experiments

Improved resource management in distributed MBQC

Abstract

Measurement-Based Quantum Computing (MBQC) is inherently well-suited for Distributed Quantum Computing (DQC): once a resource state is prepared and distributed across a network of quantum nodes, computation proceeds through local measurements coordinated by classical communication. However, since non-local gates acting on different Quantum Processing Units (QPUs) are a bottleneck, it is crucial to optimize the qubit assignment to minimize inter-node entanglement of the shared resource. For graph state resources shared across two QPUs, this task reduces to finding bipartitions with minimal cut rank. We introduce a simulated annealing-based algorithm that efficiently updates the cut rank when two vertices swap sides across a bipartition, such that computing the new cut rank from scratch, which would be much more expensive, is not necessary. We show that the approach is highly effective for determining qubit assignments in distributed MBQC by testing it on grid graphs and the measurement-based Quantum Approximate Optimization Algorithm (QAOA).