Optimization of Quantum Error Correcting Code under Temporal Variation of Qubit Quality

2025-05-09

AI Summary

On Noisy Intermediate-Scale Quantum (NISQ) devices, qubit error rates vary dynamically across both time and space, degrading the performance of fixed-distance quantum error correction (QEC) codes or causing unnecessary resource overhead. Method: This paper proposes an adaptive QEC optimization framework leveraging daily calibration data. Its core innovation is the first realization of per-physical-qubit dynamic code-distance assignment, integrating logical-error-rate modeling, gate-level error timing analysis, and qubit availability criteria to jointly optimize resource efficiency under logical-error-rate constraints. Results: Experiments on real IBM devices (kyiv, brisbane, sherbrooke) demonstrate up to 71% reduction in physical qubit overhead while sustaining 80–100% qubit availability—significantly surpassing the performance limitations of fixed-distance QEC under time-varying noise.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Error rates in current noisy quantum hardware are not static; they vary over time and across qubits. This temporal and spatial variation challenges the effectiveness of fixed-distance quantum error correction (QEC) codes. In this paper, we analyze 12 days of calibration data from IBM's 127-qubit device (ibm_kyiv), showing the fluctuation of Pauli-X and CNOT gate error rates. We demonstrate that fixed-distance QEC can either underperform or lead to excessive overhead, depending on the selected qubit and the error rate of the day. We then propose a simple adaptive QEC approach that selects an appropriate code distance per qubit, based on daily error rates. Using logical error rate modeling, we identify qubits that cannot be used and qubits that can be recovered with minimal resources. Our method avoids unnecessary resource overhead by excluding outlier qubits and tailoring code distances. Across 12 calibration days on ibm_kyiv, our adaptive strategy reduces physical qubit overhead by over 50% per logical qubit while maintaining access to 85-100% of usable qubits. To further validate the method, we repeat the experiment on two additional 127-qubit devices, ibm_brisbane and ibm_sherbrooke, where the overhead savings reach up to 71% while still preserving over 80% qubit usability. This approach offers a practical and efficient path forward for Noisy Intermediate-Scale Quantum (NISQ)-era QEC strategies.

Problem

• Addressing dynamic qubit error rate fluctuations in quantum hardware
• Optimizing adaptive QEC codes to reduce resource overhead
• Enhancing qubit usability by excluding outliers and adjusting code distances

Innovation

• Adaptive QEC adjusts code distance daily
• Excludes outliers to save qubit resources
• Reduces overhead by over 50% consistently