Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.
Quantum technologies for climate change: Preliminary assessment
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Hybrid variational quantum algorithm solves the advection-diffusion equation on small systems using current noisy IBM quantum hardware, with claimed logarithmic scaling in vector space dimension.
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Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.
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Variational Quantum Solutions to the Advection-Diffusion Equation for Applications in Fluid Dynamics
Hybrid variational quantum algorithm solves the advection-diffusion equation on small systems using current noisy IBM quantum hardware, with claimed logarithmic scaling in vector space dimension.