Adding a log-overhead regularizer with trainable cutting angles reduces circuit-cutting sampling cost in QML regression, at similar reported accuracy, but without an unregularized baseline.
Constructing a virtual two-qubit gate by sampling single-qubit operations,
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CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting
Adding a log-overhead regularizer with trainable cutting angles reduces circuit-cutting sampling cost in QML regression, at similar reported accuracy, but without an unregularized baseline.