  There is an increasing interest in quantum algorithms for optimization
problems. Within convex optimization, interior-point methods and other recently
proposed quantum algorithms are non-trivial to implement on noisy quantum
devices. Here, we discuss how to utilize an alternative approach to convex
optimization, in general, and semidefinite programming (SDP), in particular.
This approach is based on a randomized variant of the cutting-plane method. We
show how to leverage quantum speed-up of an eigensolver in speeding up an SDP
solver utilizing the cutting-plane method. For the first time, we demonstrate a
practical implementation of a randomized variant of the cutting-plane method
for semidefinite programming on instances from SDPLIB, a well-known benchmark.
Furthermore, we show that the RCP method is very robust to noise in the
boundary oracle, which may make RCP suitable for use even on noisy quantum
devices.
