High-Fidelity Magic-State Preparation with a Biased-Noise Architecture

TL;DR

This paper presents a new initialization protocol for magic state preparation in biased-noise quantum architectures, achieving quadratic error rate improvements and significantly reducing resource costs for quantum computation.

Contribution

The authors introduce a bias-aware error-detecting code protocol that enhances the fidelity of magic states, reducing distillation overheads in biased-noise quantum systems.

Findings

Achieves a logical error rate of O(10^{-8}) after a single distillation round.

Delivers two orders of magnitude lower error rates compared to conventional methods.

Demonstrates effectiveness at modest physical error rates with realistic hardware parameters.

Abstract

Magic state distillation is a resource intensive subroutine that consumes noisy input states to produce high-fidelity resource states that are used to perform logical operations in practical quantum-computing architectures. The resource cost of magic state distillation can be reduced by improving the fidelity of the raw input states. To this end, we propose an initialization protocol that offers a quadratic improvement in the error rate of the input magic states in architectures with biased noise. This is achieved by preparing an error-detecting code which detects the dominant errors that occur during state preparation. We obtain this advantage by exploiting the native gate operations of an underlying qubit architecture that experiences biases in its noise profile. We perform simulations to analyze the performance of our protocol with the XZZX surface code. Even at modest physical parameters with a two-qubit gate error rate of $0.7\%$ and total probability of dominant errors in the gate $O(10^3)$ larger compared to that of non-dominant errors, we find that our preparation scheme delivers magic states with logical error rate $O(10^{-8})$ after a single round of the standard 15-to-1 distillation protocol; two orders of magnitude lower than using conventional state preparation. Our approach therefore promises considerable savings in overheads with near-term technology.