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Researchers use transfer learning to model quantum device noise with few samples

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a few-shot transfer learning method to model noise on quantum devices, addressing limitations in error mitigation strategies. By training a residual neural network on data from an IBM quantum device, they demonstrated that noise models can be effectively transferred to a different device with minimal fine-tuning data. This approach showed a significant improvement in recovering ideal circuit outcomes, with CX gate error and readout error identified as primary causes of cross-device mismatches. AI

IMPACT Enables more robust quantum error mitigation by adapting noise models across different hardware with limited data.

RANK_REASON Academic paper detailing a novel approach to quantum noise modeling using transfer learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers use transfer learning to model quantum device noise with few samples

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Academic paper detailing a novel approach to quantum noise modeling using transfer learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik ·

    Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware

    arXiv:2604.24397v1 Announce Type: cross Abstract: In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply noise mod…

  2. arXiv cs.LG TIER_1 English(EN) · Azizur Rahman Anik ·

    Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware

    In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply noise models learned on one quantum device to a different d…

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