  Data streaming, in which a large dataset is received as a "stream" of
updates, is an important model in the study of space-bounded computation.
Starting with the work of Le Gall [SPAA `06], it has been known that quantum
streaming algorithms can use asymptotically less space than their classical
counterparts for certain problems. However, so far, all known examples of
quantum advantages in streaming are for problems that are either specially
constructed for that purpose, or require many streaming passes over the input.
  We give a one-pass quantum streaming algorithm for one of the best studied
problems in classical graph streaming - the triangle counting problem.
Almost-tight parametrized upper and lower bounds are known for this problem in
the classical setting; our algorithm uses polynomially less space in certain
regions of the parameter space, resolving a question posed by Jain and Nayak in
2014 on achieving quantum advantages for natural streaming problems.
