The paper introduces Quantum‑Harbor, a virtual laboratory that lets AI agents interact with quantum systems in a controlled setting, enabling verification of their actions and conclusions. Using this platform, the authors created QIQCBench, a benchmark of 49 expert‑authored tasks covering calibration, control, error correction, compilation, sensing, and networking. Testing 17 state‑of‑the‑art agentic systems on QIQCBench revealed wide variation in verified performance, highlighting a gap between demonstrated capability and reliable operation and positioning Quantum‑Harbor as a foundation for measuring progress toward verified autonomy in quantum engineering.
arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.
arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.
arXiv:2607. 25834v1 Announce Type: cross Abstract: Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities.
arXiv:2605. 27729v2 Announce Type: cross Abstract: The 2024-2025 Nobel and Turing awards recognised AI and quantum science simultaneously.
arXiv:2607. 25145v1 Announce Type: cross Abstract: We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv:2607. 25865v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing.
arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.
Qlippy is a retrieval‑augmented generative AI assistant designed to support reproducible quantum software development. It is embedded in the development environment and grounds its responses in a curated corpus of quantum‑software‑engineering knowledge, explaining reproducibility and provenance concepts in context. The assistant augments Qiskit programs with MLflow‑based experiment tracking that follows the QProv schema, thereby reducing reliance on large language models and enabling low‑cost, privacy‑preserving local deployment.
arXiv:2607. 15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities.
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.
QEncodeBench evaluates whether large language models can translate classical constraint problems into verified quantum phase oracles. The benchmark measures the correctness of generated circuits using an adversarial self‑validated verifier that checks full solution‑set equivalence while enforcing resource limits. Results show that models lacking a reasoning mode perform poorly, whereas enabling native reasoning improves accuracy tenfold; semantic errors dominate, and neuro‑symbolic pipelines close most gaps by delegating critical composition to deterministic procedures.