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From the 1 of 20 linked papers with an AI index.

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20 papers

physics.chem-ph2026

TNASS: Tensor Network Active Space Selection with the Entanglement Feature

Angus Mingare, Isabelle Heuzé, Isabelle Heuzé +1

The quality of multi-scale modelling techniques in molecular electronic structure calculations, such as embedding and subspace methods, relies upon the chosen active space. The aut…

physics.flu-dyn2026

Explainable quantum-compressed machine learning for complex fluid flows

Xiao Xue, Maida Wang, Mingyang Gao +2

Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep…

quant-ph2026

The arrow of time, irreversibility, equilibrium and measurement in quantum mechanics

Christopher J. N. Coveney, Peter V. Coveney

Quantum mechanics is widely recognised as being incomplete. It is not consistent with the second law of thermodynamics and does not provide a scientifically credible physical accou…

quant-ph2026

Quantum-Accelerated Self-Consistent Field: A Hybrid Algorithm

Alexis Ralli, Tim Weaving, Thomas M. Bickley +2

The paper proposes a hybrid quantum‑classical algorithm, GAS‑SCF, that uses Grover‑based adaptive search and amplitude amplification to accelerate self‑consistent field calculation…

quant-ph2026

Practical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning

Maida Wang, Xiao Xue, Minh Chung +1

Early quantum devices can deliver a practical advantage before fault tolerance. The role we identify is a statistical module within a classical scientific workflow: a compressed me…

cs.AI2026

On the Smallness of the Large Language Models Scaling Exponents

Sauro Succi, Peter V. Coveney, Alex Hansen

We discuss reasons why the scaling exponents of current Large Language Models (LLMs) applications are indicating an unsustainable regime in terms of energy resources. We further sh…