Adiabatic graph-state quantum computation

TL;DR

This paper introduces adiabatic graph-state quantum computation (AGQC), a novel method converting measurement-based quantum computation on graph states into adiabatic holonomic processes, exploring its properties and trade-offs.

Contribution

It presents a method to convert MBQC with gflow into adiabatic holonomic computation, establishing a new framework called AGQC and analyzing its properties and trade-offs.

Findings

AGQC can simulate MBQC on graph states with gflow.

There is a trade-off between adiabatic steps and Hamiltonian properties.

Orderings different from standard MBQC affect AGQC performance.

Abstract

Measurement-based quantum computation (MBQC) and holonomic quantum computation (HQC) are two very different computational methods. The computation in MBQC is driven by adaptive measurements executed in a particular order on a large entangled state. In contrast in HQC the system starts in the ground subspace of a Hamiltonian which is slowly changed such that a transformation occurs within the subspace. Following the approach of Bacon and Flammia, we show that any measurement-based quantum computation on a graph state with \emph{gflow} can be converted into an adiabatically driven holonomic computation, which we call \emph{adiabatic graph-state quantum computation} (AGQC). We then investigate how properties of AGQC relate to the properties of MBQC, such as computational depth. We identify a trade-off that can be made between the number of adiabatic steps in AGQC and the norm of $\dot{H}$ as well as the degree of $H$, in analogy to the trade-off between the number of measurements and classical post-processing seen in MBQC. Finally the effects of performing AGQC with orderings that differ from standard MBQC are investigated.