  Since the advent of the Internet, quantifying the relative importance of web
pages is at the core of search engine methods. According to one algorithm,
PageRank, the worldwide web structure is represented by the Google matrix,
whose principal eigenvector components assign a numerical value to web pages
for their ranking. Finding such a dominant eigenvector on an ever-growing
number of web pages becomes a computationally intensive task incompatible with
Moore's Law. We demonstrate that special-purpose optical machines such as
networks of optical parametric oscillators, lasers, and gain-dissipative
condensates, may aid in accelerating the reliable reconstruction of principal
eigenvectors of real-life web graphs. We discuss the feasibility of simulating
the PageRank algorithm on large Google matrices using such unconventional
hardware. We offer alternative rankings based on the minimisation of spin
Hamiltonians. Our estimates show that special-purpose optical machines may
provide dramatic improvements in power consumption over classical computing
architectures.
