Scalability enhancement of quantum computing under limited connectivity through distributed quantum computing

TL;DR

This paper demonstrates how distributed quantum computing can improve scalability under limited connectivity by benchmarking entanglement-assisted DQC with noise modeling and analytical fidelity approximations.

Contribution

It introduces a noise-based analytical method to evaluate and optimize the scalability of distributed quantum computing with limited connectivity.

Findings

DQC shows scalability advantages over single-QPU computing.

Analytical fidelity approximation aligns with numerical simulations.

A simple formula helps estimate and optimize DQC performance.

Abstract

We employ quantum-volume random-circuit sampling to benchmark the two-QPU entanglement-assisted distributed quantum computing (DQC) and compare it with single-QPU quantum computing. We first specify a single-qubit depolarizing noise model in the random circuit. Based on this error model, we show the one-to-one correspondence of three figures of merits, namely average gate fidelity, heavy output probability, and linear cross-entropy. We derive an analytical approximation of the average gate fidelity under the specified noise model, which is shown to align with numerical simulations. The approximation is calculated based on a noise propagation matrix obtained from the extended connectivity graph of a DQC device. In numerical simulation, we unveil the scalability enhancement in DQC for the QPUs with limited connectivity. Furthermore, we provide a simple formula to estimate the average gate fidelity, which also provides us with a heuristic method to evaluate the scalability enhancement in DQC, and a guide to optimize the structure of a DQC configuration.