  Identification of up to $d$ defective items and up to $h$ inhibitors in a set
of $n$ items is the main task of non-adaptive group testing with inhibitors. To
efficiently reduce the cost of this Herculean task, a subset of the $n$ items
is formed and then tested. This is called \textit{group testing}. A test
outcome on a subset of items is positive if the subset contains at least one
defective item and no inhibitors, and negative otherwise. We present two
decoding schemes for efficiently identifying the defective items and the
inhibitors in the presence of $e$ erroneous outcomes in time $\mathsf{poly}(d,
h, e, \log_2{n})$, which is sublinear to the number of items $n$. This decoding
complexity significantly improves the state-of-the-art schemes in which the
decoding time is linear to the number of items $n$, i.e., $\mathsf{poly}(d, h,
e, n)$. Moreover, each column of the measurement matrices associated with the
proposed schemes can be nonrandomly generated in polynomial order of the number
of rows. As a result, one can save space for storing them. Simulation results
confirm our theoretical analysis. When the number of items is sufficiently
large, the decoding time in our proposed scheme is smallest in comparison with
existing work. In addition, when some erroneous outcomes are allowed, the
number of tests in the proposed scheme is often smaller than the number of
tests in existing work.
