MoSAIC: Scalable Probabilistic Error Cancellation via Variational Blockwise Noise Aggregation
Maya Ma, Rimika Jaiswal, Murphy Yuezhen Niu

TL;DR
MoSAIC is a scalable quantum error mitigation framework that reduces sampling costs of probabilistic error cancellation by partitioning circuits into noise-aligned blocks and applying variational noise modeling, enabling larger system mitigation.
Contribution
MoSAIC introduces a novel blockwise aggregation method that maintains unbiased PEC while significantly lowering sampling overhead, validated on IBM quantum hardware.
Findings
MoSAIC achieves 1-2 orders of magnitude better accuracy than standard PEC.
It enables mitigation of larger quantum systems beyond standard PEC capabilities.
Experimental validation on 156-qubit hardware demonstrates practical scalability.
Abstract
Quantum error mitigation is essential for extracting trustworthy results from noisy intermediate-scale quantum (NISQ) processors. Yet, current approaches face a core scalability bottleneck: unbiased methods such as probabilistic error cancellation (PEC) incur exponential sampling overhead, while approximate techniques like zero-noise extrapolation trade accuracy for efficiency. We introduce and experimentally demonstrate MoSAIC (Modular Spatio-temporal Aggregation for Inverted Channels), a scalable quantum error mitigation framework that preserves the unbiasedness of PEC while dramatically reducing sampling costs. MoSAIC partitions a circuit into noise-aligned blocks, learns an effective block noise model using classical variational optimization, and applies quasi-probabilistic inversion once per block instead of after every layer. This blockwise aggregation reduces both sampling…
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