  We propose and experimentally demonstrate an approximate universal-NOT
(U-NOT) operation that is robust against operational errors. In our proposal,
the U-NOT operation is composed of stochastic unitary operations represented by
the vertices of regular polyhedrons. The operation is designed to be robust
against random operational errors by increasing the number of unitary
operations (i.e., reference axes). Remarkably, no increase in the total number
of measurements nor additional resources are required to perform the U-NOT
operation. Our method can be applied in general to reduce operational errors to
an arbitrary degree of precision when approximating any anti-unitary operation
in a stochastic manner.
