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An introduction to Bayesian simulation-based inference for quantum machine learning with examples

Ivana Nikoloska, Osvaldo Simeone • Frontiers in Quantum Science and Technology (2024)

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of designing algorithms that optimize the sim...

DOAJ Open Access (DOAJ) DOI: 10.3389/frqst.2024.1394533

Exploiting subspace constraints and ab initio variational methods for quantum chemistry

Cica Gustiani, Richard Meister, Simon C Benjamin • New Journal of Physics (2023)

Variational methods offer a highly promising route to exploiting quantum computers for chemistry tasks. Here we employ methods described in a sister paper to the present report, entitled exploring ab initio machine synthesis of quantum circ...

DOAJ Open Access (DOAJ) DOI: 10.1088/1367-2630/ace182