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Research PaperResearchia:202609.16013

Evaluating Verified Autonomy in Quantum Engineering

Naixu Guo

Abstract

Reliable quantum engineering is essential for turning quantum phenomena into practical technologies. As quantum platforms grow in scale and complexity, their characterization and operation require increasing human effort and coordination. Scientific artificial intelligence agents, which can plan experiments, operate instruments, and analyze observations, offer a promising route towards autonomous quantum engineering. Yet whether current agents can perform reliably in this setting has not been sy...

Submitted: September 16, 2026Subjects: AI; Artificial Intelligence

Description / Details

Reliable quantum engineering is essential for turning quantum phenomena into practical technologies. As quantum platforms grow in scale and complexity, their characterization and operation require increasing human effort and coordination. Scientific artificial intelligence agents, which can plan experiments, operate instruments, and analyze observations, offer a promising route towards autonomous quantum engineering. Yet whether current agents can perform reliably in this setting has not been systematically established. To fill this gap, we developed Quantum-Harbor, a virtual laboratory that provides a controlled execution environment for agents to interact with quantum systems. This design enables direct verification of both the actions taken and the conclusions drawn. Building on this framework, we introduce QIQCBench, a benchmark of 4949 expert-authored tasks spanning multiple layers including calibration and control, error correction and compilation, sensing and networking. Across 1717 frontier agentic systems, QIQCBench reveals wide variation in verified performance. These results expose a substantial gap between demonstrating capability and achieving reliable operation, and establish Quantum-Harbor as a foundation for measuring progress towards verified autonomy in quantum engineering.


Source: arXiv:2609.17439v1 - http://arxiv.org/abs/2609.17439v1 PDF: https://arxiv.org/pdf/2609.17439v1 Original Link: http://arxiv.org/abs/2609.17439v1

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Submission Info
Date:
Sep 16, 2026
Topic:
Artificial Intelligence
Area:
AI
Comments:
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