Evaluating Verified Autonomy in Quantum Engineering
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Abstract (arXiv)
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 49 expert-authored tasks spanning multiple layers including calibration and control, error correction and compilation, sensing and networking. Across 17 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.
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References · 17
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
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- 2022Classical surrogates for quantum learning modelscited by 39
- 2020Machine learning enables completely automatic tuning of a quantum device faster than human expertscited by 23
- 2024Agents for self-driving laboratories applied to quantum computingcited by 18
- 2024QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Designcited by 14
- 2022Assessing requirements to scale to practical quantum advantagecited by 10
- 2018Efficiently measuring a quantum device using machine learningcited by 9
- 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
- 2026QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understandingcited by 9
- 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
5 more
- 2025Towards autonomous quantum physics research using LLM agents with access to intelligent toolscited by 7
- 2025Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processorscited by 3
- 2026gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomycited by 3
- 2026Agentic AI for Scientific Reasoning in Autonomous Quantum Sensing Experimentscited by 1
- 2026Lowering the implementation barrier of neutral-atom quantum computing with agentic workflowscited by 1
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- 2026QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing
- 2026Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization
- 2026An LLM System for Autonomous Variational Quantum Circuit Design
- 2026QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents
- 2025QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
- 2026MerLean: An Agentic Framework for Autoformalization in Quantum Computation