  We demonstrate how one can use machine learning techniques to bypass the
technical difficulties of designing an experiment and translating its outcomes
into concrete claims about fundamental features of quantum fields. In practice,
all measurements of quantum fields are carried out through local probes.
Despite measuring only a small portion of the field, such local measurements
have the capacity to reveal many of the field's global features. This is
because, when in equilibrium with their environments, quantum fields store
global information locally, albeit in a scrambled way. We show that neural
networks can be trained to unscramble this information from data generated from
a very simple one-size-fits-all local measurement protocol. To illustrate this
general claim we will consider three non-trivial features of the field as case
studies: a) how, as long as the field is in a stationary state, a particle
detector can learn about the field's boundary conditions even before signals
have time to propagate from the boundary to the detector, b) how detectors can
determine the temperature of the quantum field even without thermalizing with
it, and c) how detectors can distinguish between Fock states and coherent
states even when the first and second moments of all their quadrature operators
match. Each of these examples uses the exact same simple fixed local
measurement protocol and machine-learning ansatz successfully. This supports
the claim that the framework proposed here can be applied to nearly any kind of
local measurement on a quantum field to reveal nearly any of the field's global
properties in a one-size-fits-all manner.
