A closed-loop circuit-cutting framework that adapts partitioning, shot allocation, and estimator choice to live noise drift reports variance contraction and about 1% confidentiality overhead in simulation and emulation.
CaliScalpel: In-Situ and Fine-Grained Qubit Calibration Integrated with Surface Code Quantum Error Correction
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abstract
Quantum Error Correction (QEC) is a cornerstone of fault-tolerant, large-scale quantum computing. However, qubit error drift significantly degrades QEC performance over time, necessitating periodic calibration. Traditional calibration methods disrupt quantum states, requiring system downtime and making in situ calibration infeasible. We present CaliScalpel, an innovative framework for in situ calibration in surface codes. The core idea behind CaliScalpel is leveraging code deformation to isolate qubits undergoing calibration from logical patches. This allows calibration to proceed concurrently with computation, while code enlargement maintains error correction capabilities with minimal qubit overhead. Additionally, CaliScalpel incorporates optimized calibration schedules derived from detailed device characterization, effectively minimizing physical error rates. Our results show that CaliScalpel achieves concurrent calibration and computation with modest qubit overhead and negligible execution time impact, marking a significant step toward practical in situ calibration in surface-code-based quantum computing systems.
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MAESTROCUT: Dynamic, Noise-Adaptive, and Secure Quantum Circuit Cutting on Near-Term Hardware
A closed-loop circuit-cutting framework that adapts partitioning, shot allocation, and estimator choice to live noise drift reports variance contraction and about 1% confidentiality overhead in simulation and emulation.