Opportunities and challenges in scaling quantum error detection on hardware
2026-05-04 • Emerging Technologies
Emerging Technologies
AI summaryⓘ
The authors studied how quantum error detection, a method to fix mistakes in quantum computers, works in practice on real devices. They found that while this technique can greatly improve accuracy as you use bigger codes, it also gets more expensive in terms of samples and computing power. They tested their ideas on real and simulated quantum machines with up to 74 qubits and mapped out when these methods are effective. Overall, their work helps clarify both the benefits and challenges of using quantum error detection on current and future hardware.
quantum error detectionquantum error correctioncode distancerepetition codetriangular color codelogical qubitsquantum hardwareerror mitigationpseudothresholdquantum noise
Authors
Yanis Le Fur, Ethan Egger, Hong-Ye Hu, Vincent Russo, William J. Zeng, Ryan LaRose
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.