Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a q...
Antonio Sannia, Roberto Menta, Pratik Sathe et al.· 0 citations
This work identifies a class of cyclic stabilizer codes realizable through global iSWAP and single-qubit gates, yielding QEC thresholds nearly seven orders of magnitude larger than previous estimates for globally-controlled arrays.
Linear regression is one of the simplest and most widely used tools to learn patterns from data: it fits a set of coefficients so that a linear combination of predictors best matches observed responses. The quality of the fit is measured by the residual sum of squares, the total squared mismatch between predictions and...
Silvia Bartolucci, F. Caccioli, F. Caravelli et al.· 0 citations
We show that global optimal control can drastically suppress the impact of decoherence in globally driven superconducting quantum computing architectures, taking as a prototype a recently proposed quasi-two-dimensional ladder geometry. Using a tensor-network-based approach, we quantify how amplitude-damping and dephasi...
Ashkan Abedi, Roberto Menta, Julien Despres et al.· 0 citations
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