VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
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REALISTA generates semantically coherent adversarial prompts via latent-space optimization over input-dependent editing directions, achieving stronger hallucination elicitation than prior realistic attacks on open-source and reasoning LLMs.
An optimization-driven parametric curve method using Fourier-Chebyshev basis simulates realistic limbless locomotion with energy constraints for physical plausibility.
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.
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Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.