Opportunities and challenges in scaling quantum error detection on hardware

2026-05-04

AI Summary

Quantum error detection faces significant challenges in scalability, including exponentially growing sampling and post-processing overheads as well as constant resource costs from encoding that may compromise accuracy. This work presents the first systematic benchmarking of repetition and three-qubit bit-flip codes for logical memory and computation on medium-scale real and simulated noisy quantum hardware, encompassing up to 74 physical qubits. The authors introduce a pseudo-threshold estimation method to quantitatively assess code efficacy. Their experiments uncover both the current scalability limitations and the latent potential of existing devices, offering crucial empirical insights to guide the design of future fault-tolerant quantum architectures.

Abstract

Quantum error detection can produce unbiased expectation values that exponentially converge to noiseless results as the code distance is increased. Despite this, its performance as an error mitigation technique is relatively understudied on quantum hardware because of its two main drawbacks: (i) the number of samples increases exponentially in the circuit depth/noise level, and (ii) the classical processing generally grows exponentially in the code distance, though exceptions exist. Additionally, the constant (but often large) overhead of embedding the code and logical operations on hardware can make accuracy worse instead of better. In this work, we seek to provide a clear picture of these opportunities and challenges for scaling quantum error detection on hardware. We do so by performing a detailed benchmarking study on real and simulated noisy quantum computers, using the repetition code and triangular color code for memory experiments and logical computations with up to $74$ physical qubits. In addition to these benchmarks, we estimate the pseudothreshold of codes to map the frontier of error detection on current and future quantum computers. Despite the challenges, our results show strong promise for scaling quantum error detection on hardware.

Problem

• quantum error detection
• hardware overhead
• exponential sampling
• classical processing
• code distance

Innovation

• quantum error detection
• pseudothreshold
• hardware benchmarking
• logical qubits
• noisy quantum computers