Quantum optical systems are poised to become integral components
of technologies of the future. While there is growing commercial
interest in these systems---for applications in information
processing, secure communication and precision metrology---there
remain significant technical challenges to overcome before
widespread adoption is possible. In this thesis we consider the
general problem of optimising quantum optical systems, with a
focus on sensing and information processing applications. We
investigate four different classes of system with varying degrees
of generality and complexity, and demonstrate four corresponding
optimisation techniques.
At the most specific end of the spectrum---where behaviour is
best understood---we consider the problem of interferometric
sensitivity enhancement, specifically in the context of
long-baseline gravitational wave detectors. We investigate the
use of an auxiliary optomechanical system to generate squeezed
light exhibiting frequency-dependent quadrature rotation. Such
rotation is necessary to evade the effect of quantum back action
and achieve broadband sensitivity beyond the standard quantum
limit. We find that a cavity optomechanical system is generally
unsuitable for this purpose, since the quadrature rotation occurs
in the opposite direction to that required for broadband
sensitivity improvement.
Next we introduce a general technique to engineer arbitrary
optical spring potentials in cavity optomechanical systems. This
technique has the potential to optimise many types of sensors
relying on the optical spring effect. As an example, we show that
this technique could yield an enhancement in sensitivity by a
factor of 5 when applied to a certain gravitational sensor based
on a levitated cavity mirror.
We then consider a particular nanowire-based optomechanical
system with potential applications in force sensing. We
demonstrate a variety of ways to improve its sensitivity to
transient forces. We first apply a non-stationary feedback
cooling protocol to the system, and achieve an improvement in
peak signal-to-noise ratio by a factor of 3, corresponding to a
force resolution of 0.2fN. We then implement two non-stationary
estimation schemes, which involve post-processing data taken in
the absence of physical feedback cooling, to achieve a comparable
enhancement in performance without the need for additional
experimental complexity.
Finally, to address the most complex of systems, we present a
general-purpose machine learning algorithm capable of
automatically modelling and optimising arbitrary physical systems
without human input. To demonstrate the potential of the
algorithm we apply it to a magneto-optical trap used for a
quantum memory, and achieve an improvement in optical depth from
138 to 448.
The four techniques presented differ significantly in their style
and the types of systems to which they are applicable.
Successfully harnessing the full range of such optimisation
procedures will be vital in unlocking the potential of quantum
optical systems in the technologies of the futur