VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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In the large-N limit, spin squeezing torsion yields a nonlinear qubit governed by the two-state Gross-Pitaevskii equation that solves single-input state discrimination on the Bloch sphere.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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From spin squeezing to fast state discrimination
In the large-N limit, spin squeezing torsion yields a nonlinear qubit governed by the two-state Gross-Pitaevskii equation that solves single-input state discrimination on the Bloch sphere.