Simulating the performance of a distance-3 surface code in a linear ion trap

TL;DR

This paper assesses the feasibility of implementing a small surface code on a linear ion trap, optimizing measurement procedures, and analyzing error impacts to improve quantum error correction fidelity.

Contribution

It introduces a mapping of the surface code to a linear ion chain, develops a noise model, and evaluates the fidelity requirements for effective quantum error correction.

Findings

Two-qubit gate fidelity > 99.9% needed for logical memory advantage

Optimized measurement mapping reduces stabilizer measurement time

Error analysis identifies critical error sources affecting correction

Abstract

We explore the feasibility of implementing a small surface code with 9 data qubits and 8 ancilla qubits, commonly referred to as surface-17, using a linear chain of 171Yb+ ions. Two-qubit gates can be performed between any two ions in the chain with gate time increasing linearly with ion distance. Measurement of the ion state by fluorescence requires that the ancilla qubits be physically separated from the data qubits to avoid errors on the data due to scattered photons. We minimize the time required to measure one round of stabilizers by optimizing the mapping of the two-dimensional surface code to the linear chain of ions. We develop a physically motivated Pauli error model that allows for fast simulation and captures the key sources of noise in an ion trap quantum computer including gate imperfections and ion heating. Our simulations showed a consistent requirement of a two-qubit gate fidelity of > 99.9% for logical memory to have a better fidelity than physical two-qubit operations. Finally, we perform an analysis on the error subsets from the importance sampling method used to approximate the logical error rates in this paper to gain insight into which error sources are particularly detrimental to error correction.