  Compute-and-forward is a promising strategy to tackle interference and obtain
high rates between the transmitting users in a wireless network. However, the
quality of the wireless channels between the users substantially limits the
achievable computation rate in such systems. In this paper, we introduce the
idea of using intelligent reflecting surfaces (IRSs) to enhance the computing
capability of the compute-and-forward systems. For this purpose, we consider a
multiple access channel(MAC) where a number of users aim to send data to a base
station (BS) in a wireless network, where the BS is interested in decoding a
linear combination of the data from different users in the corresponding finite
field. Considering the compute-and-forward framework, we show that through
carefully designing the IRS parameters, such a scenario's computation rate can
be significantly improved. More specifically, we formulate an optimization
problem which aims to maximize the computation rate of the system through
optimizing the IRS phase shift parameters. We then propose an alternating
optimization (AO) approach to solve the formulated problem with low complexity.
Finally, via various numerical results, we demonstrate the effectiveness of the
IRS technology for enhancing the performance of the compute-and-forward
systems, which indicates its great potential for future wireless networks with
massive computation requirements, such as 6G.
