  Detecting abrupt changes in data streams is crucial because they are often
triggered by events that have important consequences if left unattended.
Quickest change point detection has become a vital sequential analysis
primitive that aims at designing procedures that minimize the expected
detection delay of a change subject to a bounded expected false alarm time. We
put forward the quantum counterpart of this fundamental primitive on streams of
quantum data. We give a lower-bound on the mean minimum delay when the expected
time of a false alarm is asymptotically large, under the most general quantum
detection strategy, which is given by a sequence of adaptive collective
(potentially weak) measurements on the growing string of quantum data. In
addition, we give particular strategies based on repeated measurements on
independent blocks of samples, that asymptotically attain the lower-bound, and
thereby establish the ultimate quantum limit for quickest change point
detection. Finally, we discuss online change point detection in quantum
channels.
