  Quantum state estimation for continuously monitored dynamical systems
involves assigning a quantum state to an individual system at some time,
conditioned on the results of continuous observations. The quality of the
estimation depends on how much observed information is used and on how
optimality is defined for the estimate. In this work, we consider problems of
quantum state estimation where some of the measurement records are not
available, but where the available records come from both before (past) and
after (future) the estimation time, enabling better estimates than is possible
using the past information alone. Past-future information for quantum systems
has been used in various ways in the literature, in particular, the quantum
state smoothing, the most-likely path, and the two-state vector and related
formalisms. To unify these seemingly unrelated approaches, we propose a
framework for partially-observed quantum system with continuous monitoring,
wherein the first two existing formalisms can be accommodated, with some
generalization. The unifying framework is based on state estimation with
expected cost minimization, where the cost can be defined either in the space
of the unknown record or in the space of the unknown true state. Moreover, we
connect all three existing approaches conceptually by defining five new cost
functions, and thus new types of estimators, which bridge the gaps between
them. We illustrate the applicability of our method by calculating all seven
estimators we consider for the example of a driven two-level system
dissipatively coupled to bosonic baths. Our theory also allows connections to
classical state estimation, which create further conceptual links between our
quantum state estimators.
