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New QCalEval benchmark tests vision-language models on quantum calibration plots

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced QCalEval, a new benchmark designed to assess how well vision-language models (VLMs) can interpret quantum computing calibration plots. The benchmark includes 243 samples across various quantum computing experiment types and is evaluated using both zero-shot and in-context learning methods. Initial results show that while frontier closed-source models perform well, many open-weight models struggle with multi-image in-context learning, and supervised fine-tuning alone does not fully bridge this gap. AI

IMPACT Establishes a new evaluation standard for VLMs in scientific domains, potentially guiding future model development for specialized data interpretation.

RANK_REASON This is a research paper introducing a new benchmark for evaluating VLMs on a specific scientific task.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New QCalEval benchmark tests vision-language models on quantum calibration plots

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

    Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEv…

  2. arXiv cs.CV TIER_1 English(EN) · Shuxiang Cao, Zijian Zhang, Abhishek Agarwal, Grace Bratrud, Niyaz R. Beysengulov, Daniel C. Cole, Alejandro G\'omez Frieiro, Elena O. Glen, Hao Hsu, Gang Huang, Raymond Jow, Greshma Shaji, Tom Lubowe, Ligeng Zhu, Luis Mantilla Calder\'on, Nicola Pancotti ·

    QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

    arXiv:2604.25884v1 Announce Type: cross Abstract: Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language…

  3. arXiv cs.CV TIER_1 English(EN) · Krysta Svore ·

    QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

    Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEv…

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